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Bidirectional Projection Network for Cross Dimension Scene Understanding | Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong | 2D image representations are in regular grids and can be processed efficiently, whereas 3D point clouds are unordered and scattered in 3D space. The information inside these two visual domains is well complementary, e.g., 2D images have fine-grained texture while 3D point clouds contain plentiful geometry information. However, most current visual recognition systems process them individually. In this paper, we present a bidirectional projection network (BPNet) for joint 2D and 3D reasoning in an end-to-end manner. It contains 2D and 3D sub-networks with symmetric architectures, that are connected by our proposed bidirectional projection module (BPM). Via the BPM, complementary 2D and 3D information can interact with each other in multiple architectural levels, such that advantages in these two visual domains can be combined for better scene recognition. Extensive quantitative and qualitative experimental evaluations show that joint reasoning over 2D and 3D visual domains can benefit both 2D and 3D scene understanding simultaneously. Our BPNet achieves top performance on the ScanNetV2 benchmark for both 2D and 3D semantic segmentation. Code is available at https://github.com/wbhu/BPNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Bidirectional_Projection_Network_for_Cross_Dimension_Scene_Understanding_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14326 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Bidirectional_Projection_Network_for_Cross_Dimension_Scene_Understanding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Bidirectional_Projection_Network_for_Cross_Dimension_Scene_Understanding_CVPR_2021_paper.html | CVPR 2021 | null | null |
Event-Based Synthetic Aperture Imaging With a Hybrid Network | Xiang Zhang, Wei Liao, Lei Yu, Wen Yang, Gui-Song Xia | Synthetic aperture imaging (SAI) is able to achieve the see through effect by blurring out the off-focus foreground occlusions and reconstructing the in-focus occluded targets from multi-view images. However, very dense occlusions and extreme lighting conditions may bring significant disturbances to the SAI based on conventional frame-based cameras, leading to performance degeneration. To address these problems, we propose a novel SAI system based on the event camera which can produce asynchronous events with extremely low latency and high dynamic range. Thus, it can eliminate the interference of dense occlusions by measuring with almost continuous views, and simultaneously tackle the over/under exposure problems. To reconstruct the occluded targets, we propose a hybrid encoder-decoder network composed of spiking neural networks (SNNs) and convolutional neural networks (CNNs). In the hybrid network, the spatio-temporal information of the collected events is first encoded by SNN layers, and then transformed to the visual image of the occluded targets by a style-transfer CNN decoder. Through experiments, the proposed method shows remarkable performance in dealing with very dense occlusions and extreme lighting conditions, and high quality visual images can be reconstructed using pure event data. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Event-Based_Synthetic_Aperture_Imaging_With_a_Hybrid_Network_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02376 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Event-Based_Synthetic_Aperture_Imaging_With_a_Hybrid_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Event-Based_Synthetic_Aperture_Imaging_With_a_Hybrid_Network_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Event-Based_Synthetic_Aperture_CVPR_2021_supplemental.pdf | null |
RSG: A Simple but Effective Module for Learning Imbalanced Datasets | Jianfeng Wang, Thomas Lukasiewicz, Xiaolin Hu, Jianfei Cai, Zhenghua Xu | Imbalanced datasets widely exist in practice and are a great challenge for training deep neural models with a good generalization on infrequent classes. In this work, we propose a new rare-class sample generator (RSG) to solve this problem. RSG aims to generate some new samples for rare classes during training, and it has in particular the following advantages: (1) it is convenient to use and highly versatile, because it can be easily integrated into any kind of convolutional neural network, and it works well when combined with different loss functions, and (2) it is only used during the training phase, and therefore, no additional burden is imposed on deep neural networks during the testing phase. In extensive experimental evaluations, we verify the effectiveness of RSG. Furthermore, by leveraging RSG, we obtain competitive results on Imbalanced CIFAR and new state-of-the-art results on Places-LT, ImageNet-LT, and iNaturalist 2018. The source code is available at https://github.com/Jianf-Wang/RSG. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_RSG_A_Simple_but_Effective_Module_for_Learning_Imbalanced_Datasets_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.09859 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_RSG_A_Simple_but_Effective_Module_for_Learning_Imbalanced_Datasets_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_RSG_A_Simple_but_Effective_Module_for_Learning_Imbalanced_Datasets_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_RSG_A_Simple_CVPR_2021_supplemental.pdf | null |
Learning Statistical Texture for Semantic Segmentation | Lanyun Zhu, Deyi Ji, Shiping Zhu, Weihao Gan, Wei Wu, Junjie Yan | Existing semantic segmentation works mainly focus on learning the contextual information in high-level semantic features with CNNs. In order to maintain a precise boundary, low-level texture features are directly skip-connected into the deeper layers. Nevertheless, texture features are not only about local structure, but also include global statistical knowledge of the input image. In this paper, we fully take advantages of the low-level texture features and propose a novel Statistical Texture Learning Network (STLNet) for semantic segmentation. For the first time, STLNet analyzes the distribution of low level information and efficiently utilizes them for the task. Specifically, a novel Quantization and Counting Operator (QCO) is designed to describe the texture information in a statistical manner. Based on QCO, two modules are introduced: (1) Texture Enhance Module (TEM), to capture texture-related information and enhance the texture details; (2) Pyramid Texture Feature Extraction Module (PTFEM), to effectively extract the statistical texture features from multiple scales. Through extensive experiments, we show that the proposed STLNet achieves state-of-the-art performance on three semantic segmentation benchmarks: Cityscapes, PASCAL Context and ADE20K. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Learning_Statistical_Texture_for_Semantic_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.04133 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Learning_Statistical_Texture_for_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Learning_Statistical_Texture_for_Semantic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Neural Feature Search for RGB-Infrared Person Re-Identification | Yehansen Chen, Lin Wan, Zhihang Li, Qianyan Jing, Zongyuan Sun | RGB-Infrared person re-identification (RGB-IR ReID) is a challenging cross-modality retrieval problem, which aims at matching the person-of-interest over visible and infrared camera views. Most existing works achieve performance gains through manually-designed feature selection modules, which often require significant domain knowledge and rich experience. In this paper, we study a general paradigm, termed Neural Feature Search (NFS), to automate the process of feature selection. Specifically, NFS combines a dual-level feature search space and a differentiable search strategy to jointly select identity-related cues in coarse-grained channels and fine-grained spatial pixels. This combination allows NFS to adaptively filter background noises and concentrate on informative parts of human bodies in a data-driven manner. Moreover, a cross-modality contrastive optimization scheme further guides NFS to search features that can minimize modality discrepancy whilst maximizing inter-class distance. Extensive experiments on mainstream benchmarks demonstrate that our method outperforms state-of-the-arts, especially achieving better performance on the RegDB dataset with significant improvement of 11.20% and 8.64% in Rank-1 and mAP, respectively. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Neural_Feature_Search_for_RGB-Infrared_Person_Re-Identification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02366 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Neural_Feature_Search_for_RGB-Infrared_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Neural_Feature_Search_for_RGB-Infrared_Person_Re-Identification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Neural_Feature_Search_CVPR_2021_supplemental.pdf | null |
FP-NAS: Fast Probabilistic Neural Architecture Search | Zhicheng Yan, Xiaoliang Dai, Peizhao Zhang, Yuandong Tian, Bichen Wu, Matt Feiszli | Differential Neural Architecture Search (NAS) requires all layer choices to be held in memory simultaneously; this limits the size of both search space and final architecture. In contrast, Probabilistic NAS, such as PARSEC, learns a distribution over high-performing architectures, and uses only as much memory as needed to train a single model. Nevertheless, it needs to sample many architectures, making it computationally expensive for searching in an extensive space. To solve these problems, we propose a sampling method adaptive to the distribution entropy, drawing more samples to encourage explorations at the beginning, and reducing samples as learning proceeds. Furthermore, to search fast in the multi-variate space, we propose a coarse-to-fine strategy by using a factorized distribution at the beginning which can reduce the number of architecture parameters by over an order of magnitude. We call this method Fast Probabilistic NAS (FP-NAS). Compared with PARSEC, it can sample 64% fewer architectures and search 2.1x faster. Compared with FBNetV2, FP-NAS is 1.9x - 3.5x faster, and the searched models outperform FBNetV2 models on ImageNet. FP-NAS allows us to expand the giant FBNetV2 space to be wider (i.e. larger channel choices) and deeper (i.e. more blocks), while adding Split-Attention block and enabling the search over the number of splits. When searching a model of size 0.4G FLOPS, FP-NAS is 132x faster than EfficientNet, and the searched FP-NAS-L0 model outperforms EfficientNet-B0 by 0.7% accuracy. Without using any architecture surrogate or scaling tricks, we directly search large models up to 1.0G FLOPS. Our FP-NAS-L2 model with simple distillation outperforms BigNAS-XL with advanced in-place distillation by 0.7% accuracy using similar FLOPS. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_FP-NAS_Fast_Probabilistic_Neural_Architecture_Search_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_FP-NAS_Fast_Probabilistic_Neural_Architecture_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_FP-NAS_Fast_Probabilistic_Neural_Architecture_Search_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yan_FP-NAS_Fast_Probabilistic_CVPR_2021_supplemental.pdf | null |
Fast Sinkhorn Filters: Using Matrix Scaling for Non-Rigid Shape Correspondence With Functional Maps | Gautam Pai, Jing Ren, Simone Melzi, Peter Wonka, Maks Ovsjanikov | In this paper, we provide a theoretical foundation for pointwise map recovery from functional maps and highlight its relation to a range of shape correspondence methods based on spectral alignment. With this analysis in hand, we develop a novel spectral registration technique: Fast Sinkhorn Filters, which allows for the recovery of accurate and bijective pointwise correspondences with a superior time and memory complexity in comparison to existing approaches. Our method combines the simple and concise representation of correspondence using functional maps with the matrix scaling schemes from computational optimal transport. By exploiting the sparse structure of the kernel matrices involved in the transport map computation, we provide an efficient trade-off between acceptable accuracy and complexity for the problem of dense shape correspondence. | https://openaccess.thecvf.com/content/CVPR2021/papers/Pai_Fast_Sinkhorn_Filters_Using_Matrix_Scaling_for_Non-Rigid_Shape_Correspondence_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pai_Fast_Sinkhorn_Filters_Using_Matrix_Scaling_for_Non-Rigid_Shape_Correspondence_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pai_Fast_Sinkhorn_Filters_Using_Matrix_Scaling_for_Non-Rigid_Shape_Correspondence_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pai_Fast_Sinkhorn_Filters_CVPR_2021_supplemental.pdf | null |
Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstruction | Shanyan Guan, Jingwei Xu, Yunbo Wang, Bingbing Ni, Xiaokang Yang | This paper considers a new problem of adapting a pre-trained model of human mesh reconstruction to out-of-domain streaming videos. However, most previous methods based on the parametric SMPL model underperform in new domains with unexpected, domain-specific attributes, such as camera parameters, lengths of bones, backgrounds, and occlusions. Our general idea is to dynamically fine-tune the source model on test video streams with additional temporal constraints, such that it can mitigate the domain gaps without over-fitting the 2D information of individual test frames. A subsequent challenge is how to avoid conflicts between the 2D and temporal constraints. We propose to tackle this problem using a new training algorithm named Bilevel Online Adaptation (BOA), which divides the optimization process of overall multi-objective into two steps of weight probe and weight update in a training iteration. We demonstrate that BOA leads to state-of-the-art results on two human mesh reconstruction benchmarks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Guan_Bilevel_Online_Adaptation_for_Out-of-Domain_Human_Mesh_Reconstruction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16449 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Guan_Bilevel_Online_Adaptation_for_Out-of-Domain_Human_Mesh_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Guan_Bilevel_Online_Adaptation_for_Out-of-Domain_Human_Mesh_Reconstruction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Guan_Bilevel_Online_Adaptation_CVPR_2021_supplemental.pdf | null |
The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth | Jamie Watson, Oisin Mac Aodha, Victor Prisacariu, Gabriel Brostow, Michael Firman | Self-supervised monocular depth estimation networks are trained to predict scene depth using nearby frames as a supervision signal during training. However, for many applications, sequence information in the form of video frames is also available at test time. The vast majority of monocular networks do not make use of this extra signal, thus ignoring valuable information that could be used to improve the predicted depth. Those that do, either use computationally expensive test-time refinement techniques or off-the-shelf recurrent networks, which only indirectly make use of the geometric information that is inherently available. We propose ManyDepth, an adaptive approach to dense depth estimation that can make use of sequence information at test time, when it is available. Taking inspiration from multi-view stereo, we propose a deep end-to-end cost volume based approach that is trained using self-supervision only. We present a novel consistency loss that encourages the network to ignore the cost volume when it is deemed unreliable, e.g. in the case of moving objects, and an augmentation scheme to cope with static cameras. Our detailed experiments on both KITTI and Cityscapes show that we outperform all published self-supervised baselines, including those that use single or multiple frames at test time. | https://openaccess.thecvf.com/content/CVPR2021/papers/Watson_The_Temporal_Opportunist_Self-Supervised_Multi-Frame_Monocular_Depth_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.14540 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Watson_The_Temporal_Opportunist_Self-Supervised_Multi-Frame_Monocular_Depth_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Watson_The_Temporal_Opportunist_Self-Supervised_Multi-Frame_Monocular_Depth_CVPR_2021_paper.html | CVPR 2021 | null | null |
Distribution-Aware Adaptive Multi-Bit Quantization | Sijie Zhao, Tao Yue, Xuemei Hu | In this paper, we explore the compression of deep neural networks by quantizing the weights and activations into multi-bit binary networks (MBNs). A distribution-aware multi-bit quantization (DMBQ) method that incorporates the distribution prior into the optimization of quantization is proposed. Instead of solving the optimization in each iteration, DMBQ search the optimal quantization scheme over the distribution space beforehand, and select the quantization scheme during training using a fast lookup table based strategy. Based upon DMBQ, we further propose loss-guided bit-width allocation (LBA) to adaptively quantize and even prune the neural network. The first-order Taylor expansion is applied to build a metric for evaluating the loss sensitivity of the quantization of each channel, and automatically adjust the bit-width of weights and activations channel-wisely. We extend our method to image classification tasks and experimental results show that our method not only outperforms state-of-the-art quantized networks in terms of accuracy but also is more efficient in terms of training time compared with state-of-the-art MBNs, even for the extremely low bit width (below 1-bit) quantization cases. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Distribution-Aware_Adaptive_Multi-Bit_Quantization_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Distribution-Aware_Adaptive_Multi-Bit_Quantization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Distribution-Aware_Adaptive_Multi-Bit_Quantization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhao_Distribution-Aware_Adaptive_Multi-Bit_CVPR_2021_supplemental.pdf | null |
KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA | Kenneth Marino, Xinlei Chen, Devi Parikh, Abhinav Gupta, Marcus Rohrbach | One of the most challenging question types in VQA is when answering the question requires outside knowledge not present in the image. In this work we study open-domain knowledge, the setting when the knowledge required to answer a question is not given/annotated, neither at training nor test time. We tap into two types of knowledge representations and reasoning. First, implicit knowledge which can be learned effectively from unsupervised language pretraining and supervised training data with transformer-based models. Second, explicit, symbolic knowledge encoded in knowledge bases. Our approach combines both---exploiting the powerful implicit reasoning of transformer models for answer prediction, and integrating symbolic representations from a knowledge graph, while never losing their explicit semantics to an implicit embedding. We combine diverse sources of knowledge to cover the wide variety of knowledge needed to solve knowledge-based questions. We show our approach, KRISP, significantly outperforms state-of-the-art on OK-VQA, the largest available dataset for open-domain knowledge-based VQA. We show with extensive ablations that while our model successfully exploits implicit knowledge reasoning, the symbolic answer module which explicitly connects the knowledge graph to the answer vocabulary is critical to the performance of our method and generalizes to rare answers. | https://openaccess.thecvf.com/content/CVPR2021/papers/Marino_KRISP_Integrating_Implicit_and_Symbolic_Knowledge_for_Open-Domain_Knowledge-Based_VQA_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.11014 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Marino_KRISP_Integrating_Implicit_and_Symbolic_Knowledge_for_Open-Domain_Knowledge-Based_VQA_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Marino_KRISP_Integrating_Implicit_and_Symbolic_Knowledge_for_Open-Domain_Knowledge-Based_VQA_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Marino_KRISP_Integrating_Implicit_CVPR_2021_supplemental.pdf | null |
Amalgamating Knowledge From Heterogeneous Graph Neural Networks | Yongcheng Jing, Yiding Yang, Xinchao Wang, Mingli Song, Dacheng Tao | In this paper, we study a novel knowledge transfer task in the domain of graph neural networks (GNNs). We strive to train a multi-talented student GNN, without accessing human annotations, that "amalgamates" knowledge from a couple of teacher GNNs with heterogeneous architectures and handling distinct tasks. The student derived in this way is expected to integrate the expertise from both teachers while maintaining a compact architecture. To this end, we propose an innovative approach to train a slimmable GNN that enables learning from teachers with varying feature dimensions. Meanwhile, to explicitly align topological semantics between the student and teachers, we introduce a topological attribution map (TAM) to highlight the structural saliency in a graph, based on which the student imitates the teachers' ways of aggregating information from neighbors. Experiments on seven datasets across various tasks, including multi-label classification and joint segmentation-classification, demonstrate that the learned student, with a lightweight architecture, achieves gratifying results on par with and sometimes even superior to those of the teachers in their specializations. Our code is publicly available at https://github.com/ycjing/AmalgamateGNN.PyTorch. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jing_Amalgamating_Knowledge_From_Heterogeneous_Graph_Neural_Networks_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jing_Amalgamating_Knowledge_From_Heterogeneous_Graph_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jing_Amalgamating_Knowledge_From_Heterogeneous_Graph_Neural_Networks_CVPR_2021_paper.html | CVPR 2021 | null | null |
MetaSets: Meta-Learning on Point Sets for Generalizable Representations | Chao Huang, Zhangjie Cao, Yunbo Wang, Jianmin Wang, Mingsheng Long | Deep learning techniques for point clouds have achieved strong performance on a range of 3D vision tasks. However, it is costly to annotate large-scale point sets, making it critical to learn generalizable representations that can transfer well across different point sets. In this paper, we study a new problem of 3D Domain Generalization (3DDG) with the goal to generalize the model to other unseen domains of point clouds without any access to them in the training process. It is a challenging problem due to the substantial geometry shift from simulated to real data, such that most existing 3D models underperform due to overfitting the complete geometries in the source domain. We propose to tackle this problem with MetaSets, which meta-learns point cloud representations from a set of classification tasks on carefully-designed transformed point sets containing specific geometry priors. The learned representations are more generalizable to various unseen domains of different geometries. We design two benchmarks for Sim-to-Real transfer of 3D point clouds. Experimental results show that MetaSets outperforms existing 3D deep learning methods by large margins. | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_MetaSets_Meta-Learning_on_Point_Sets_for_Generalizable_Representations_CVPR_2021_paper.pdf | https://arxiv.org/abs/2204.07311 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_MetaSets_Meta-Learning_on_Point_Sets_for_Generalizable_Representations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_MetaSets_Meta-Learning_on_Point_Sets_for_Generalizable_Representations_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_MetaSets_Meta-Learning_on_CVPR_2021_supplemental.zip | https://openaccess.thecvf.com |
StEP: Style-Based Encoder Pre-Training for Multi-Modal Image Synthesis | Moustafa Meshry, Yixuan Ren, Larry S. Davis, Abhinav Shrivastava | We propose a novel approach for multi-modal Image-to-image (I2I) translation. To tackle the one-to-many relationship between input and output domains, previous works use complex training objectives to learn a latent embedding, jointly with the generator, that models the variability of the output domain. In contrast, we directly model the style variability of images, independent of the image synthesis task. Specifically, we pre-train a generic style encoder using a novel proxy task to learn an embedding of images, from arbitrary domains, into a low-dimensional style latent space. The learned latent space introduces several advantages over previous traditional approaches to multi-modal I2I translation. First, it is not dependent on the target dataset, and generalizes well across multiple domains. Second, it learns a more powerful and expressive latent space, which improves the fidelity of style capture and transfer. The proposed style pre-training also simplifies the training objective and speeds up the training significantly. Furthermore, we provide a detailed study of the contribution of different loss terms to the task of multi-modal I2I translation, and propose a simple alternative to VAEs to enable sampling from unconstrained latent spaces. Finally, we achieve state-of-the-art results on six challenging benchmarks with a simple training objective that includes only a GAN loss and a reconstruction loss. | https://openaccess.thecvf.com/content/CVPR2021/papers/Meshry_StEP_Style-Based_Encoder_Pre-Training_for_Multi-Modal_Image_Synthesis_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.07098 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Meshry_StEP_Style-Based_Encoder_Pre-Training_for_Multi-Modal_Image_Synthesis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Meshry_StEP_Style-Based_Encoder_Pre-Training_for_Multi-Modal_Image_Synthesis_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Meshry_StEP_Style-Based_Encoder_CVPR_2021_supplemental.pdf | null |
Goal-Oriented Gaze Estimation for Zero-Shot Learning | Yang Liu, Lei Zhou, Xiao Bai, Yifei Huang, Lin Gu, Jun Zhou, Tatsuya Harada | Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen classes. Since semantic knowledge is built on attributes shared between different classes, which are highly local, strong prior for localization of object attribute is beneficial for visual-semantic embedding. Interestingly, when recognizing unseen images, human would also automatically gaze at regions with certain semantic clue. Therefore, we introduce a novel goal-oriented gaze estimation module (GEM) to improve the discriminative attribute localization based on the class-level attributes for ZSL. We aim to predict the actual human gaze location to get the visual attention regions for recognizing a novel object guided by attribute description. Specifically, the task-dependent attention is learned with the goal-oriented GEM, and the global image features are simultaneously optimized with the regression of local attribute features. Experiments on three ZSL benchmarks, i.e., CUB, SUN and AWA2, show the superiority or competitiveness of our proposed method against the state-of-the-art ZSL methods. The ablation analysis on real gaze data CUB-VWSW also validates the benefits and accuracy of our gaze estimation module. This work implies the promising benefits of collecting human gaze dataset and automatic gaze estimation algorithms on high-level computer vision tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Goal-Oriented_Gaze_Estimation_for_Zero-Shot_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.03433 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Goal-Oriented_Gaze_Estimation_for_Zero-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Goal-Oriented_Gaze_Estimation_for_Zero-Shot_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
LED2-Net: Monocular 360deg Layout Estimation via Differentiable Depth Rendering | Fu-En Wang, Yu-Hsuan Yeh, Min Sun, Wei-Chen Chiu, Yi-Hsuan Tsai | Although significant progress has been made in room layout estimation, most methods aim to reduce the loss in the 2D pixel coordinate rather than exploiting the room structure in the 3D space. Towards reconstructing the room layout in 3D, we formulate the task of 360 layout estimation as a problem of predicting depth on the horizon line of a panorama. Specifically, we propose the Differentiable Depth Rendering procedure to make the conversion from layout to depth prediction differentiable, thus making our proposed model end-to-end trainable while leveraging the 3D geometric information, without the need of providing the ground truth depth. Our method achieves state-of-the-art performance on numerous 360 layout benchmark datasets. Moreover, our formulation enables a pre-training step on the depth dataset, which further improves the generalizability of our layout estimation model. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_LED2-Net_Monocular_360deg_Layout_Estimation_via_Differentiable_Depth_Rendering_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_LED2-Net_Monocular_360deg_Layout_Estimation_via_Differentiable_Depth_Rendering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_LED2-Net_Monocular_360deg_Layout_Estimation_via_Differentiable_Depth_Rendering_CVPR_2021_paper.html | CVPR 2021 | null | null |
Multi-Stage Aggregated Transformer Network for Temporal Language Localization in Videos | Mingxing Zhang, Yang Yang, Xinghan Chen, Yanli Ji, Xing Xu, Jingjing Li, Heng Tao Shen | We address the problem of localizing a specific moment from an untrimmed video by a language sentence query. Generally, previous methods mainly exist two problems that are not fully solved: 1) How to effectively model the fine-grained visual-language alignment between video and language query? 2) How to accurately localize the moment in the original video length? In this paper, we streamline the temporal language localization as a novel multi-stage aggregated transformer network. Specifically, we first introduce a new visual-language transformer backbone, which enables iterations and alignments among all elements in visual and language sequences. Different from previous multi-modal transformers, our backbone keeps both structure unified and modality specific. Moreover, we also propose a multi-stage aggregation module topped on the transformer backbone. In this module, we compute three stage-specific representations corresponding to different moment stages respectively, i.e. starting, middle and ending stages, for each video element. Then for a moment candidate, we concatenate the starting/middle/ending representations of its starting/middle/ending elements respectively to form the final moment representation. Because the obtained moment representation captures the stage specific information, it is very discriminative for accurate localization. Extensive experiments on ActivityNet Captions and TACoS datasets demonstrate our proposed method achieves significant improvements compared with all other methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Multi-Stage_Aggregated_Transformer_Network_for_Temporal_Language_Localization_in_Videos_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Multi-Stage_Aggregated_Transformer_Network_for_Temporal_Language_Localization_in_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Multi-Stage_Aggregated_Transformer_Network_for_Temporal_Language_Localization_in_Videos_CVPR_2021_paper.html | CVPR 2021 | null | null |
DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation | Xinyi Wu, Zhenyao Wu, Hao Guo, Lili Ju, Song Wang | Semantic segmentation of nighttime images plays an equally important role as that of daytime images in autonomous driving, but the former is much more challenging due to poor illuminations and arduous human annotations. In this paper, we propose a novel domain adaptation network (DANNet) for nighttime semantic segmentation without using labeled nighttime image data. It employs an adversarial training with a labeled daytime dataset and an unlabeled dataset that contains coarsely aligned day-night image pairs. Specifically, for the unlabeled day-night image pairs, we use the pixel-level predictions of static object categories on a daytime image as a pseudo supervision to segment its counterpart nighttime image. We further design a re-weighting strategy to handle the inaccuracy caused by misalignment between day-night image pairs and wrong predictions of daytime images, as well as boost the prediction accuracy of small objects. The proposed DANNet is the first one stage adaptation framework for nighttime semantic segmentation, which does not train additional day-night image transfer models as a separate pre-processing stage. Extensive experiments on Dark Zurich and Nighttime Driving datasets show that our method achieves state-of-the-art performance for nighttime semantic segmentation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_DANNet_A_One-Stage_Domain_Adaptation_Network_for_Unsupervised_Nighttime_Semantic_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.10834 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_DANNet_A_One-Stage_Domain_Adaptation_Network_for_Unsupervised_Nighttime_Semantic_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_DANNet_A_One-Stage_Domain_Adaptation_Network_for_Unsupervised_Nighttime_Semantic_CVPR_2021_paper.html | CVPR 2021 | null | null |
Dynamic Transfer for Multi-Source Domain Adaptation | Yunsheng Li, Lu Yuan, Yinpeng Chen, Pei Wang, Nuno Vasconcelos | Recent works of multi-source domain adaptation focus on learning a domain-agnostic model, of which the parameters are static. However, such a static model is difficult to handle conflicts across multiple domains, and suffers from a performance degradation in both source domains and target domain. In this paper, we present dynamic transfer to address domain conflicts, where the model parameters are adapted to samples. The key insight is that adapting model across domains is achieved via adapting model across samples. Thus, it breaks down source domain barriers and turns multi-source domains into a single source domain. This also simplifies the alignment between source and target domains, as it only requires the target domain to be aligned with any part of the union of source domains. Furthermore, we find dynamic transfer can be simply modeled by aggregating residual matrices and a static convolution matrix. Experimental results show that, without using domain labels, our dynamic transfer outperforms the state-of-the-art method by more than 3% on the large multi-source domain adaptation datasets -- DomainNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Dynamic_Transfer_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.10583 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Dynamic_Transfer_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Dynamic_Transfer_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Semi-Supervised Video Deraining With Dynamical Rain Generator | Zongsheng Yue, Jianwen Xie, Qian Zhao, Deyu Meng | While deep learning (DL)-based video deraining methods have achieved significant success recently, they still exist two major drawbacks. Firstly, most of them do not sufficiently model the characteristics of rain layers of rainy videos. In fact, the rain layers exhibit strong physical properties (e.g., direction, scale and thickness) in spatial dimension and natural continuities in temporal dimension, and thus can be generally modelled by the spatial-temporal process in statistics. Secondly, current DL-based methods seriously depend on the labeled synthetic training data, whose rain types are always deviated from those in unlabeled real data. Such gap between synthetic and real data sets leads to poor performance when applying them in real scenarios. Against these issues, this paper proposes a new semisupervised video deraining method, in which a dynamic rain generator is employed to fit the rain layer, expecting to better depict its insightful characteristics. Specifically, such dynamic generator consists of one emission model and one transition model to simultaneously encode the spatially physical structure and temporally continuous changes of rain streaks, respectively, which both are parameterized as deep neural networks (DNNs). Further more, different prior formats are designed for the labeled synthetic and unlabeled real data, so as to fully exploit the common knowledge underlying them. Last but not least, we also design a Monte Carlo EM algorithm to solve this model. Extensive experiments are conducted to verify the superiorities of the proposed semi-supervised deraining model. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yue_Semi-Supervised_Video_Deraining_With_Dynamical_Rain_Generator_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07939 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yue_Semi-Supervised_Video_Deraining_With_Dynamical_Rain_Generator_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yue_Semi-Supervised_Video_Deraining_With_Dynamical_Rain_Generator_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yue_Semi-Supervised_Video_Deraining_CVPR_2021_supplemental.pdf | null |
See Through Gradients: Image Batch Recovery via GradInversion | Hongxu Yin, Arun Mallya, Arash Vahdat, Jose M. Alvarez, Jan Kautz, Pavlo Molchanov | Training deep neural networks requires gradient estimation from data batches to update parameters. Gradients per parameter are averaged over a set of data and this has been presumed to be safe for privacy-preserving training in joint, collaborative, and federated learning applications. Prior work only showed the possibility of recovering input data given gradients under very restrictive conditions - a single input point, or a network with no non-linearities, or a small 32x32 px input batch. Therefore, averaging gradients over larger batches was thought to be safe. In this work, we introduce GradInversion, using which input images from a larger batch (8 - 48 images) can also be recovered for large networks such as ResNets (50 layers), on complex datasets such as ImageNet (1000 classes, 224x224 px). We formulate an optimization task that converts random noise into natural images, matching gradients while regularizing image fidelity. We also propose an algorithm for target class label recovery given gradients. We further propose a group consistency regularization framework, where multiple agents starting from different random seeds work together to find an enhanced reconstruction of original data batch. We show that gradients encode a surprisingly large amount of information, such that all the individual images can be recovered with high fidelity via GradInversion, even for complex datasets, deep networks, and large batch sizes. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yin_See_Through_Gradients_Image_Batch_Recovery_via_GradInversion_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.07586 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yin_See_Through_Gradients_Image_Batch_Recovery_via_GradInversion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yin_See_Through_Gradients_Image_Batch_Recovery_via_GradInversion_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yin_See_Through_Gradients_CVPR_2021_supplemental.pdf | null |
Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition | Delian Ruan, Yan Yan, Shenqi Lai, Zhenhua Chai, Chunhua Shen, Hanzi Wang | In this paper, we propose a novel Feature Decomposition and Reconstruction Learning (FDRL) method for effective facial expression recognition. We view the expression information as the combination of the shared information (expression similarities) across different expressions and the unique information (expression-specific variations) for each expression. More specifically, FDRL mainly consists of two crucial networks: a Feature Decomposition Network (FDN) and a Feature Reconstruction Network (FRN). In particular, FDN first decomposes the basic features extracted from a backbone network into a set of facial action-aware latent features to model expression similarities. Then, FRN captures the intra-feature and inter-feature relationships for latent features to characterize expression-specific variations, and reconstructs the expression feature. To this end, two modules including an intra-feature relation modeling module and an inter-feature relation modeling module are developed in FRN. Experimental results on both the in-the-lab databases (including CK+, MMI, and Oulu-CASIA) and the in-the-wild databases (including RAF-DB and SFEW) show that the proposed FDRL method consistently achieves higher recognition accuracy than several state-of-the-art methods. This clearly highlights the benefit of feature decomposition and reconstruction for classifying expressions. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ruan_Feature_Decomposition_and_Reconstruction_Learning_for_Effective_Facial_Expression_Recognition_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.05160 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ruan_Feature_Decomposition_and_Reconstruction_Learning_for_Effective_Facial_Expression_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ruan_Feature_Decomposition_and_Reconstruction_Learning_for_Effective_Facial_Expression_Recognition_CVPR_2021_paper.html | CVPR 2021 | null | null |
Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D Sequences | Norman Muller, Yu-Shiang Wong, Niloy J. Mitra, Angela Dai, Matthias Niessner | Multi-object tracking from RGB-D video sequences is a challenging problem due to the combination of changing viewpoints, motion, and occlusions over time. We observe that having the complete geometry of objects aids in their tracking, and thus propose to jointly infer the complete geometry of objects as well as track them, for rigidly moving objects over time. Our key insight is that inferring the complete geometry of the objects significantly helps in tracking. By hallucinating unseen regions of objects, we can obtain additional correspondences between the same instance, thus providing robust tracking even under strong change of appearance. From a sequence of RGB-D frames, we detect objects in each frame and learn to predict their complete object geometry as well as a dense correspondence mapping into a canonical space. This allows us to derive 6DoF poses for the objects in each frame, along with their correspondence between frames, providing robust object tracking across the RGB-D sequence. Experiments on both synthetic and real-world RGB-D data demonstrate that we achieve state-of-the-art performance on 3D multi-object tracking. Furthermore, we show that our object completion significantly helps tracking, providing an improvement of 8% in mean MOTA. | https://openaccess.thecvf.com/content/CVPR2021/papers/Muller_Seeing_Behind_Objects_for_3D_Multi-Object_Tracking_in_RGB-D_Sequences_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Muller_Seeing_Behind_Objects_for_3D_Multi-Object_Tracking_in_RGB-D_Sequences_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Muller_Seeing_Behind_Objects_for_3D_Multi-Object_Tracking_in_RGB-D_Sequences_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Muller_Seeing_Behind_Objects_CVPR_2021_supplemental.zip | null |
Multi-view Depth Estimation using Epipolar Spatio-Temporal Networks | Xiaoxiao Long, Lingjie Liu, Wei Li, Christian Theobalt, Wenping Wang | We present a novel method for multi-view depth estimation from a single video, which is a critical task in various applications, such as perception, reconstruction and robot navigation. Although previous learning-based methods have demonstrated compelling results, most works estimate depth maps of individual video frames independently, without taking into consideration the strong geometric and temporal coherence among the frames. Moreover, current state-of-the-art (SOTA) models mostly adopt a fully 3D convolution network for cost regularization and therefore require high computational cost, thus limiting their deployment in real-world applications. Our method achieves temporally coherent depth estimation results by using a novel Epipolar Spatio-Temporal (EST) transformer to explicitly associate geometric and temporal correlation with multiple estimated depth maps. Furthermore, to reduce the computational cost, inspired by recent Mixture-of-Experts models, we design a compact hybrid network consisting of a 2D context-aware network and a 3D matching network which learn 2D context information and 3D disparity cues separately. Extensive experiments demonstrate that our method achieves higher accuracy in depth estimation and significant speedup than the SOTA methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Long_Multi-view_Depth_Estimation_using_Epipolar_Spatio-Temporal_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.13118 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Long_Multi-view_Depth_Estimation_using_Epipolar_Spatio-Temporal_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Long_Multi-view_Depth_Estimation_using_Epipolar_Spatio-Temporal_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Long_Multi-view_Depth_Estimation_CVPR_2021_supplemental.pdf | null |
AutoFlow: Learning a Better Training Set for Optical Flow | Deqing Sun, Daniel Vlasic, Charles Herrmann, Varun Jampani, Michael Krainin, Huiwen Chang, Ramin Zabih, William T. Freeman, Ce Liu | Synthetic datasets play a critical role in pre-training CNN models for optical flow, but they are painstaking to generate and hard to adapt to new applications. To automate the process, we present AutoFlow, a simple and effective method to render training data for optical flow that optimizes the performance of a model on a target dataset. AutoFlow takes a layered approach to render synthetic data, where the motion, shape, and appearance of each layer are controlled by learnable hyperparameters. Experimental results show that AutoFlow achieves state-of-the-art accuracy in pre-training both PWC-Net and RAFT. Our code and data are available at autoflow-google.github.io. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_AutoFlow_Learning_a_Better_Training_Set_for_Optical_Flow_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.14544 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_AutoFlow_Learning_a_Better_Training_Set_for_Optical_Flow_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_AutoFlow_Learning_a_Better_Training_Set_for_Optical_Flow_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sun_AutoFlow_Learning_a_CVPR_2021_supplemental.zip | null |
LPSNet: A Lightweight Solution for Fast Panoptic Segmentation | Weixiang Hong, Qingpei Guo, Wei Zhang, Jingdong Chen, Wei Chu | Panoptic segmentation is a challenging task aiming to simultaneously segment objects (things) at instance level and background contents (stuff) at semantic level. Existing methods mostly utilize two-stage detection network to attain instance segmentation results, and fully convolutional network to produce semantic segmentation prediction. Post-processing or additional modules are required to handle the conflicts between the outputs from these two nets, which makes such methods suffer from low efficiency, heavy memory consumption and complicated implementation. To simplify the pipeline and decrease computation/memory cost, we propose an one-stage approach called Lightweight Panoptic Segmentation Network (LPSNet), which does not involve proposal, anchor or mask head. Instead, we predict bounding box and semantic category at each pixel upon the feature map produced by an augmented feature pyramid, and design a parameter-free head to merge the per-pixel bounding box and semantic prediction into panoptic segmentation output. Our LPSNet is not only efficient in computation and memory, but also accurate in panoptic segmentation. Comprehensive experiments on COCO, Cityscapes and Mapillary Vistas datasets demonstrate the promising effectiveness and efficiency of the proposed LPSNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hong_LPSNet_A_Lightweight_Solution_for_Fast_Panoptic_Segmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_LPSNet_A_Lightweight_Solution_for_Fast_Panoptic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_LPSNet_A_Lightweight_Solution_for_Fast_Panoptic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
You See What I Want You To See: Exploring Targeted Black-Box Transferability Attack for Hash-Based Image Retrieval Systems | Yanru Xiao, Cong Wang | With the large multimedia content online, deep hashing has become a popular method for efficient image retrieval and storage. However, by inheriting the algorithmic backend from softmax classification, these techniques are vulnerable to the well-known adversarial examples as well. The massive collection of online images into the database also opens up new attack vectors. Attackers can embed adversarial images into the database and target specific categories to be retrieved by user queries. In this paper, we start from an adversarial standpoint to explore and enhance the capacity of targeted black-box transferability attack for deep hashing. We motivate this work by a series of empirical studies to see the unique challenges in image retrieval. We study the relations between adversarial subspace and black-box transferability via utilizing random noise as a proxy. Then we develop a new attack that is simultaneously adversarial and robust to noise to enhance transferability. Our experimental results demonstrate about 1.2-3x improvements of black-box transferability compared with the state-of-the-art mechanisms. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xiao_You_See_What_I_Want_You_To_See_Exploring_Targeted_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xiao_You_See_What_I_Want_You_To_See_Exploring_Targeted_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xiao_You_See_What_I_Want_You_To_See_Exploring_Targeted_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xiao_You_See_What_CVPR_2021_supplemental.pdf | null |
The Blessings of Unlabeled Background in Untrimmed Videos | Yuan Liu, Jingyuan Chen, Zhenfang Chen, Bing Deng, Jianqiang Huang, Hanwang Zhang | Weakly-supervised Temporal Action Localization (WTAL) aims to detect the action segments with only video-level action labels in training. The key challenge is how to distinguish the action of interest segments from the background, which is unlabelled even on the video-level. While previous works treat the background as "curses", we consider it as "blessings". Specifically, we first use causal analysis to point out that the common localization errors are due to the unobserved confounder that resides ubiquitously in visual recognition. Then, we propose a Temporal Smoothing PCA-based (TS-PCA) deconfounder, which exploits the unlabelled background to model an observed substitute for the unobserved confounder, to remove the confounding effect. Note that the proposed deconfounder is model-agnostic and non-intrusive, and hence can be applied in any WTAL method without model re-designs. Through extensive experiments on four state-of-the-art WTAL methods, we show that the deconfounder can improve all of them on the public datasets: THUMOS-14 and ActivityNet-1.3. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_The_Blessings_of_Unlabeled_Background_in_Untrimmed_Videos_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13183 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_The_Blessings_of_Unlabeled_Background_in_Untrimmed_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_The_Blessings_of_Unlabeled_Background_in_Untrimmed_Videos_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_The_Blessings_of_CVPR_2021_supplemental.pdf | null |
Autoregressive Stylized Motion Synthesis With Generative Flow | Yu-Hui Wen, Zhipeng Yang, Hongbo Fu, Lin Gao, Yanan Sun, Yong-Jin Liu | Motion style transfer is an important problem in many computer graphics and computer vision applications, including human animation, games, and robotics. Most existing deep learning methods for this problem are supervised and trained by registered motion pairs. In addition, these methods are often limited to yielding a deterministic output, given a pair of style and content motions. In this paper, we propose an unsupervised approach for motion style transfer by synthesizing stylized motions autoregressively using a generative flow model M. M is trained to maximize the exact likelihood of a collection of unlabeled motions, based on an autoregressive context of poses in previous frames and a control signal representing the movement of a root joint. Thanks to invertible flow transformations, latent codes that encode deep properties of motion styles are efficiently inferred by M. By combining the latent codes (from an input style motion S) with the autoregressive context and control signal (from an input content motion C), M outputs a stylized motion which transfers style from S to C. Moreover, our model is probabilistic and is able to generate various plausible motions with a specific style. We evaluate the proposed model on motion capture datasets containing different human motion styles. Experiment results show that our model outperforms the state-of-the-art methods, despite not requiring manually labeled training data. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wen_Autoregressive_Stylized_Motion_Synthesis_With_Generative_Flow_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wen_Autoregressive_Stylized_Motion_Synthesis_With_Generative_Flow_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wen_Autoregressive_Stylized_Motion_Synthesis_With_Generative_Flow_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wen_Autoregressive_Stylized_Motion_CVPR_2021_supplemental.zip | null |
Improving Multiple Object Tracking With Single Object Tracking | Linyu Zheng, Ming Tang, Yingying Chen, Guibo Zhu, Jinqiao Wang, Hanqing Lu | Despite considerable similarities between multiple object tracking (MOT) and single object tracking (SOT) tasks, modern MOT methods have not benefited from the development of SOT ones to achieve satisfactory performance. The major reason for this situation is that it is inappropriate and inefficient to apply multiple SOT models directly to the MOT task, although advanced SOT methods are of the strong discriminative power and can run at fast speeds. In this paper, we propose a novel and end-to-end trainable MOT architecture that extends CenterNet by adding an SOT branch for tracking objects in parallel with the existing branch for object detection, allowing the MOT task to benefit from the strong discriminative power of SOT methods in an effective and efficient way. Unlike most existing SOT methods which learn to distinguish the target object from its local backgrounds, the added SOT branch trains a separate SOT model per target online to distinguish the target from its surrounding targets, assigning SOT models the novel discrimination. Moreover, similar to the detection branch, the SOT branch treats objects as points, making its online learning efficient even if multiple targets are processed simultaneously. Without tricks, the proposed tracker achieves MOTAs of 0.710 and 0.686, IDF1s of 0.719 and 0.714, on MOT17 and MOT20 benchmarks, respectively, while running at 16 FPS on MOT17. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Improving_Multiple_Object_Tracking_With_Single_Object_Tracking_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Improving_Multiple_Object_Tracking_With_Single_Object_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Improving_Multiple_Object_Tracking_With_Single_Object_Tracking_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_Improving_Multiple_Object_CVPR_2021_supplemental.pdf | null |
Memory Oriented Transfer Learning for Semi-Supervised Image Deraining | Huaibo Huang, Aijing Yu, Ran He | Deep learning based methods have shown dramatic improvements in image rain removal by using large-scale paired data of synthetic datasets. However, due to the various appearances of real rain streaks that may be different from those in the synthetic training data, it is challenging to directly extend existing methods to the real-world scenes. To address this issue, we propose a memory-oriented semi-supervised (MOSS) method which enables the network to explore and exploit the properties of rain streaks from both synthetic and real data. The key aspect of our method is designing an encoder-decoder neural network that is augmented with a self-supervised memory module, where items in the memory record the prototypical patterns of rain degradations and are updated in a self-supervised way. Consequently, the rainy styles can be comprehensively derived from synthetic or real-world degraded images without the need for clean labels. Furthermore, we present a self-training mechanism that attempts to transfer deraining knowledge from supervised rain removal to unsupervised cases. An additional target network, which is updated with an exponential moving average of the online deraining network, is utilized to produce pseudo-labels for unlabeled rainy images. Meanwhile, the deraining network is optimized with supervised objectives on both synthetic paired data and pseudo-paired noisy data. Extensive experiments show that the proposed method achieves more appealing results not only on limited labeled data but also on unlabeled real-world images than recent state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Memory_Oriented_Transfer_Learning_for_Semi-Supervised_Image_Deraining_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Memory_Oriented_Transfer_Learning_for_Semi-Supervised_Image_Deraining_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Memory_Oriented_Transfer_Learning_for_Semi-Supervised_Image_Deraining_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_Memory_Oriented_Transfer_CVPR_2021_supplemental.pdf | null |
Instance Localization for Self-Supervised Detection Pretraining | Ceyuan Yang, Zhirong Wu, Bolei Zhou, Stephen Lin | Prior research on self-supervised learning has led to considerable progress on image classification, but often with degraded transfer performance on object detection. The objective of this paper is to advance self-supervised pretrained models specifically for object detection. Based on the inherent difference between classification and detection, we propose a new self-supervised pretext task, called instance localization. Image instances are pasted at various locations and scales onto background images. The pretext task is to predict the instance category given the composited images as well as the foreground bounding boxes. We show that integration of bounding boxes into pretraining promotes better alignment between convolutional features and region boxes. In addition, we propose an augmentation method on the bounding boxes to further enhance this feature alignment. As a result, our model becomes weaker at Imagenet semantic classification but stronger at image patch localization, with an overall stronger pretrained model for object detection. Experimental results demonstrate that our approach yields state-of-the-art transfer learning results for object detection on PASCAL VOC and MSCOCO. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Instance_Localization_for_Self-Supervised_Detection_Pretraining_CVPR_2021_paper.pdf | http://arxiv.org/abs/2102.08318 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Instance_Localization_for_Self-Supervised_Detection_Pretraining_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Instance_Localization_for_Self-Supervised_Detection_Pretraining_CVPR_2021_paper.html | CVPR 2021 | null | null |
Adaptive Methods for Real-World Domain Generalization | Abhimanyu Dubey, Vignesh Ramanathan, Alex Pentland, Dhruv Mahajan | Invariant approaches have been remarkably successful in tackling the problem of domain generalization, where the objective is to perform inference on data distributions different from those used in training. In our work, we investigate whether it is possible to leverage domain information from the unseen test samples themselves. We propose a domain-adaptive approach consisting of two steps: a) we first learn a discriminative domain embedding from unsupervised training examples, and b) use this domain embedding as supplementary information to build a domain-adaptive model, that takes both the input as well as its domain into account while making predictions. For unseen domains, our method simply uses few unlabelled test examples to construct the domain embedding. This enables adaptive classification on any unseen domain. Our approach achieves state-of-the-art performance on various domain generalization benchmarks. In addition, we introduce the first real-world, large-scale domain generalization benchmark, Geo-YFCC, containing 1.1M samples over 40 training, 7 validation and 15 test domains, orders of magnitude larger than prior work. We show that the existing approaches either do not scale to this dataset or underperform compared to the simple baseline of training a model on the union of data from all training domains. In contrast, our approach achieves a significant 1% improvement. | https://openaccess.thecvf.com/content/CVPR2021/papers/Dubey_Adaptive_Methods_for_Real-World_Domain_Generalization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15796 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Dubey_Adaptive_Methods_for_Real-World_Domain_Generalization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Dubey_Adaptive_Methods_for_Real-World_Domain_Generalization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dubey_Adaptive_Methods_for_CVPR_2021_supplemental.pdf | null |
Deep Animation Video Interpolation in the Wild | Li Siyao, Shiyu Zhao, Weijiang Yu, Wenxiu Sun, Dimitris Metaxas, Chen Change Loy, Ziwei Liu | In the animation industry, cartoon videos are usually produced at low frame rate since hand drawing of such frames is costly and time-consuming. Therefore, it is desirable to develop computational models that can automatically interpolate the in-between animation frames. However, existing video interpolation methods fail to produce satisfying results on animation data. Compared to natural videos, animation videos possess two unique characteristics that make frame interpolation difficult: 1) cartoons comprise lines and smooth color pieces. The smooth areas lack textures and make it difficult to estimate accurate motions on animation videos. 2) cartoons express stories via exaggeration. Some of the motions are non-linear and extremely large. In this work, we formally define and study the animation video interpolation problem for the first time. To address the aforementioned challenges, we propose an effective framework, AnimeInterp, with two dedicated modules in a coarse-to-fine manner. Specifically, 1) Segment-Guided Matching resolves the "lack of textures" challenge by exploiting global matching among color pieces that are piece-wise coherent. 2) Recurrent Flow Refinement resolves the "non-linear and extremely large motion" challenge by recurrent predictions using a transformer-like architecture. To facilitate comprehensive training and evaluations, we build a large-scale animation triplet dataset, ATD-12K, which comprises 12,000 triplets with rich annotations. Extensive experiments demonstrate that our approach outperforms existing state-of-the-art interpolation methods for animation videos. Notably, AnimeInterp shows favorable perceptual quality and robustness for animation scenarios in the wild. The proposed dataset and code are available at https://github.com/lisiyao21/AnimeInterp/. | https://openaccess.thecvf.com/content/CVPR2021/papers/Siyao_Deep_Animation_Video_Interpolation_in_the_Wild_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02495 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Siyao_Deep_Animation_Video_Interpolation_in_the_Wild_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Siyao_Deep_Animation_Video_Interpolation_in_the_Wild_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Siyao_Deep_Animation_Video_CVPR_2021_supplemental.pdf | null |
Isometric Multi-Shape Matching | Maolin Gao, Zorah Lahner, Johan Thunberg, Daniel Cremers, Florian Bernard | Finding correspondences between shapes is a fundamental problem in computer vision and graphics, which is relevant for many applications, including 3D reconstruction, object tracking, and style transfer. The vast majority of correspondence methods aim to find a solution between pairs of shapes, even if multiple instances of the same class are available. While isometries are often studied in shape correspondence problems, they have not been considered explicitly in the multi-matching setting. This paper closes this gap by proposing a novel optimisation formulation for isometric multi-shape matching. We present a suitable optimisation algorithm for solving our formulation and provide a convergence and complexity analysis. Moreover, our algorithm obtains multi-matchings that are cycle-consistent without having to explicitly enforce cycle-consistency constraints. We demonstrate the superior performance of our method on various datasets and set the new state-of-the-art in isometric multi-shape matching. | https://openaccess.thecvf.com/content/CVPR2021/papers/Gao_Isometric_Multi-Shape_Matching_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.02689 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_Isometric_Multi-Shape_Matching_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_Isometric_Multi-Shape_Matching_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gao_Isometric_Multi-Shape_Matching_CVPR_2021_supplemental.pdf | null |
Spatially Consistent Representation Learning | Byungseok Roh, Wuhyun Shin, Ildoo Kim, Sungwoong Kim | Self-supervised learning has been widely used to obtain transferrable representations from unlabeled images. Especially, recent contrastive learning methods have shown impressive performances on downstream image classification tasks. While these contrastive methods mainly focus on generating invariant global representations at the image-level under semantic-preserving transformations, they are prone to overlook spatial consistency of local representations and therefore have a limitation in pretraining for localization tasks such as object detection and instance segmentation. Moreover, aggressively cropped views used in existing contrastive methods can minimize representation distances between the semantically different regions of a single image. In this paper, we propose a spatially consistent representation learning algorithm (SCRL) for multi-object and location-specific tasks. In particular, we devise a novel self-supervised objective that tries to produce coherent spatial representations of a randomly cropped local region according to geometric translations and zooming operations. On various downstream localization tasks with benchmark datasets, the proposed SCRL shows significant performance improvements over the image-level supervised pretraining as well as the state-of-the-art self-supervised learning methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Roh_Spatially_Consistent_Representation_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.06122 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Roh_Spatially_Consistent_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Roh_Spatially_Consistent_Representation_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Roh_Spatially_Consistent_Representation_CVPR_2021_supplemental.pdf | null |
Semantic Scene Completion via Integrating Instances and Scene In-the-Loop | Yingjie Cai, Xuesong Chen, Chao Zhang, Kwan-Yee Lin, Xiaogang Wang, Hongsheng Li | Semantic Scene Completion aims at reconstructing a complete 3D scene with precise voxel-wise semantics from a single-view depth or RGBD image. It is a crucial but challenging problem for indoor scene understanding. In this work, we present a novel framework named Scene-Instance-Scene Network (SISNet), which takes advantages of both instance and scene level semantic information. Our method is capable of inferring fine-grained shape details as well as nearby objects whose semantic categories are easily mixedup. The key insight is that we decouple the instances from a coarsely completed semantic scene instead of a raw input image to guide the reconstruction of instances and the overall scene. SISNet conducts iterative scene-to-instance (SI) and instance-to-scene (IS) semantic completion. Specifically, the SI is able to encode objects' surrounding context for effectively decoupling instances from the scene and each instance could be voxelized into higher resolution to capture finer details. With IS, fine-grained instance information can be integrated back into the 3D scene and thus leads to more accurate semantic scene completion. Utilizing such an iterative mechanism, the scene and instance completion benefits each other to achieve higher completion accuracy. Extensively experiments show that our proposed method consistently outperforms state-of-the-art methods on both real NYU, NYUCAD and synthetic SUNCG-RGBD datasets. The code and the supplementary material will be available at https://github.com/yjcaimeow/SISNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cai_Semantic_Scene_Completion_via_Integrating_Instances_and_Scene_In-the-Loop_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03640 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cai_Semantic_Scene_Completion_via_Integrating_Instances_and_Scene_In-the-Loop_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cai_Semantic_Scene_Completion_via_Integrating_Instances_and_Scene_In-the-Loop_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cai_Semantic_Scene_Completion_CVPR_2021_supplemental.pdf | null |
Efficient Deformable Shape Correspondence via Multiscale Spectral Manifold Wavelets Preservation | Ling Hu, Qinsong Li, Shengjun Liu, Xinru Liu | The functional map framework has proven to be extremely effective for representing dense correspondences between deformable shapes. A key step in this framework is to formulate suitable preservation constraints to encode the geometric information that must be preserved by the unknown map. For this issue, we construct novel and powerful constraints to determine the functional map, where multiscale spectral manifold wavelets are required to be preserved at each scale correspondingly. Such constraints allow us to extract significantly more information than previous methods, especially those based on descriptor preservation constraints, and strongly ensure the isometric property of the map. In addition, we also propose a remarkable efficient iterative method to alternatively update the functional maps and pointwise maps. Moreover, when we use the tight wavelet frames in iterations, the computation of the functional maps boils down to a simple filtering procedure with low-pass and various band-pass filters, which avoids time-consuming solving large systems of linear equations commonly presented in functional maps. We demonstrate on a wide variety of experiments with different datasets that our approach achieves significant improvements both in the shape correspondence quality and the computing efficiency. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Efficient_Deformable_Shape_Correspondence_via_Multiscale_Spectral_Manifold_Wavelets_Preservation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Efficient_Deformable_Shape_Correspondence_via_Multiscale_Spectral_Manifold_Wavelets_Preservation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Efficient_Deformable_Shape_Correspondence_via_Multiscale_Spectral_Manifold_Wavelets_Preservation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hu_Efficient_Deformable_Shape_CVPR_2021_supplemental.pdf | null |
TearingNet: Point Cloud Autoencoder To Learn Topology-Friendly Representations | Jiahao Pang, Duanshun Li, Dong Tian | Topology matters. Despite the recent success of point cloud processing with geometric deep learning, it remains arduous to capture the complex topologies of point cloud data with a learning model. Given a point cloud dataset containing objects with various genera, or scenes with multiple objects, we propose an autoencoder, TearingNet, which tackles the challenging task of representing the point clouds using a fixed-length descriptor. Unlike existing works directly deforming predefined primitives of genus zero (e.g., a 2D square patch) to an object-level point cloud, our TearingNet is characterized by a proposed Tearing network module and a Folding network module interacting with each other iteratively. Particularly, the Tearing network module learns the point cloud topology explicitly. By breaking the edges of a primitive graph, it tears the graph into patches or with holes to emulate the topology of a target point cloud, leading to faithful reconstructions. Experimentation shows the superiority of our proposal in terms of reconstructing point clouds as well as generating more topology-friendly representations than benchmarks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Pang_TearingNet_Point_Cloud_Autoencoder_To_Learn_Topology-Friendly_Representations_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.10187 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pang_TearingNet_Point_Cloud_Autoencoder_To_Learn_Topology-Friendly_Representations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pang_TearingNet_Point_Cloud_Autoencoder_To_Learn_Topology-Friendly_Representations_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pang_TearingNet_Point_Cloud_CVPR_2021_supplemental.pdf | null |
Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics | Yanzhao Wu, Ling Liu, Zhongwei Xie, Ka-Ho Chow, Wenqi Wei | Neural network ensembles are gaining popularity by harnessing the complementary wisdom of multiple base models. Ensemble teams with high diversity promote high failure independence, which is effective for boosting the overall ensemble accuracy. This paper provides an in-depth study on how to design and compute ensemble diversity, which can capture the complementary decision capacity of ensemble member models. We make three original contributions. First, we revisit the ensemble diversity metrics in the literature and analyze the inherent problems of poor correlation between ensemble diversity and ensemble accuracy, which leads to the low quality ensemble selection using such diversity metrics. Second, instead of computing diversity scores for ensemble teams of different sizes using the same criteria, we introduce focal model based ensemble diversity metrics, coined as FQ-diversity metrics. Our new metrics significantly improve the intrinsic correlation between high ensemble diversity and high ensemble accuracy. Third, we introduce a diversity fusion method, coined as the EQ-diversity metric, by integrating the top three most representative FQ-diversity metrics. Comprehensive experiments on two benchmark datasets (CIFAR-10 and ImageNet) show that our FQ and EQ diversity metrics are effective for selecting high diversity ensemble teams to boost overall ensemble accuracy. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Boosting_Ensemble_Accuracy_by_Revisiting_Ensemble_Diversity_Metrics_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Boosting_Ensemble_Accuracy_by_Revisiting_Ensemble_Diversity_Metrics_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Boosting_Ensemble_Accuracy_by_Revisiting_Ensemble_Diversity_Metrics_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wu_Boosting_Ensemble_Accuracy_CVPR_2021_supplemental.pdf | null |
WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition | Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, Junjie Huang, Xinze Chen, Jiagang Zhu, Tian Yang, Jiwen Lu, Dalong Du, Jie Zhou | In this paper, we contribute a new million-scale face benchmark containing noisy 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) training data, as well as an elaborately designed time-constrained evaluation protocol. Firstly, we collect 4M name list and download 260M faces from the Internet. Then, a Cleaning Automatically utilizing Self-Training (CAST) pipeline is devised to purify the tremendous WebFace260M, which is efficient and scalable. To the best of our knowledge, the cleaned WebFace42M is the largest public face recognition training set and we expect to close the data gap between academia and industry. Referring to practical scenarios, Face Recognition Under Inference Time conStraint (FRUITS) protocol and a test set are constructed to comprehensively evaluate face matchers. Equipped with this benchmark, we delve into million-scale face recognition problems. A distributed framework is developed to train face recognition models efficiently without tampering with the performance. Empowered by WebFace42M, we reduce relative 40% failure rate on the challenging IJB-C set, and rank the 3rd among 430 entries on NIST-FRVT. Even 10% data (WebFace4M) shows superior performance compared with public training set. Furthermore, comprehensive baselines are established on our rich-attribute test set under FRUITS-100ms/500ms/1000ms protocol, including MobileNet, EfficientNet, AttentionNet, ResNet, SENet, ResNeXt and RegNet families. Benchmark website is https://www.face-benchmark.org. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_WebFace260M_A_Benchmark_Unveiling_the_Power_of_Million-Scale_Deep_Face_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.04098 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_WebFace260M_A_Benchmark_Unveiling_the_Power_of_Million-Scale_Deep_Face_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_WebFace260M_A_Benchmark_Unveiling_the_Power_of_Million-Scale_Deep_Face_CVPR_2021_paper.html | CVPR 2021 | null | null |
RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection | Pei Sun, Weiyue Wang, Yuning Chai, Gamaleldin Elsayed, Alex Bewley, Xiao Zhang, Cristian Sminchisescu, Dragomir Anguelov | The detection of 3D objects from LiDAR data is a critical component in most autonomous driving systems. Safe, high speed driving needs larger detection ranges, which are enabled by new LiDARs. These larger detection ranges require more efficient and accurate detection models. Towards this goal, we propose Range Sparse Net (RSN) - a simple, efficient, and accurate 3D object detector - in order to tackle real time 3D object detection in this extended detection regime. RSN predicts foreground points from range images and applies sparse convolutions on the selected fore-ground points to detect objects. The lightweight 2D convolutions on dense range images results in significantly fewer selected foreground points, thus enabling the later sparse convolutions in RSN to efficiently operate. Combining features from the range image further enhance detection ac-curacy. RSN runs at more than 60 frames per second on a 150mx150m detection region on Waymo Open Dataset (WOD) while being more accurate than previously published detectors. RSN is ranked first in the WOD leaderboard based on the APH/LEVEL1 metrics for LiDAR-based pedestrian and vehicle detection, while being several times faster than alternatives. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_RSN_Range_Sparse_Net_for_Efficient_Accurate_LiDAR_3D_Object_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_RSN_Range_Sparse_Net_for_Efficient_Accurate_LiDAR_3D_Object_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_RSN_Range_Sparse_Net_for_Efficient_Accurate_LiDAR_3D_Object_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sun_RSN_Range_Sparse_CVPR_2021_supplemental.pdf | null |
Labeled From Unlabeled: Exploiting Unlabeled Data for Few-Shot Deep HDR Deghosting | K. Ram Prabhakar, Gowtham Senthil, Susmit Agrawal, R. Venkatesh Babu, Rama Krishna Sai S Gorthi | High Dynamic Range (HDR) deghosting is an indispensable tool in capturing wide dynamic range scenes without ghosting artifacts. Recently, convolutional neural networks (CNNs) have shown tremendous success in HDR deghosting. However, CNN-based HDR deghosting methods require collecting large datasets with ground truth, which is a tedious and time-consuming process. This paper proposes a pioneering work by introducing zero and few-shot learning strategies for data-efficient HDR deghosting. Our approach consists of two stages of training. In stage one, we train the model with few labeled (5 or less) dynamic samples and a pool of unlabeled samples with a self-supervised loss. We use the trained model to predict HDRs for the unlabeled samples. To derive data for the next stage of training, we propose a novel method for generating corresponding dynamic inputs from the predicted HDRs of unlabeled data. The generated artificial dynamic inputs and predicted HDRs are used as paired labeled data. In stage two, we finetune the model with the original few labeled data and artificially generated labeled data. Our few-shot approach outperforms many fully-supervised methods in two publicly available datasets, using as little as five labeled dynamic samples. | https://openaccess.thecvf.com/content/CVPR2021/papers/Prabhakar_Labeled_From_Unlabeled_Exploiting_Unlabeled_Data_for_Few-Shot_Deep_HDR_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Prabhakar_Labeled_From_Unlabeled_Exploiting_Unlabeled_Data_for_Few-Shot_Deep_HDR_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Prabhakar_Labeled_From_Unlabeled_Exploiting_Unlabeled_Data_for_Few-Shot_Deep_HDR_CVPR_2021_paper.html | CVPR 2021 | null | null |
Convolutional Dynamic Alignment Networks for Interpretable Classifications | Moritz Bohle, Mario Fritz, Bernt Schiele | We introduce a new family of neural network models called Convolutional Dynamic Alignment Networks (CoDA-Nets), which are performant classifiers with a high degree of inherent interpretability. Their core building blocks are Dynamic Alignment Units (DAUs), which linearly transform their input with weight vectors that dynamically align with task-relevant patterns. As a result, CoDA-Nets model the classification prediction through a series of input-dependent linear transformations, allowing for linear decomposition of the output into individual input contributions. Given the alignment of the DAUs, the resulting contribution maps align with discriminative input patterns. These model-inherent decompositions are of high visual quality and outperform existing attribution methods under quantitative metrics. Further, CoDA-Nets constitute performant classifiers, achieving on par results to ResNet and VGG models on e.g. CIFAR-10 and TinyImagenet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Bohle_Convolutional_Dynamic_Alignment_Networks_for_Interpretable_Classifications_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00032 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bohle_Convolutional_Dynamic_Alignment_Networks_for_Interpretable_Classifications_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bohle_Convolutional_Dynamic_Alignment_Networks_for_Interpretable_Classifications_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bohle_Convolutional_Dynamic_Alignment_CVPR_2021_supplemental.pdf | null |
EDNet: Efficient Disparity Estimation With Cost Volume Combination and Attention-Based Spatial Residual | Songyan Zhang, Zhicheng Wang, Qiang Wang, Jinshuo Zhang, Gang Wei, Xiaowen Chu | Existing state-of-the-art disparity estimation works mostly leverage the 4D concatenation volume and construct a very deep 3D convolution neural network (CNN) for disparity regression, which is inefficient due to the high memory consumption and slow inference speed. In this paper, we propose a network named EDNet for efficient disparity estimation. Firstly, we construct a combined volume which incorporates contextual information from the squeezed concatenation volume and feature similarity measurement from the correlation volume. The combined volume can be next aggregated by 2D convolutions which are faster and require less memory than 3D convolutions. Secondly, we propose an attention-based spatial residual module to generate attention-aware residual features. The attention mechanism is applied to provide intuitive spatial evidence about inaccurate regions with the help of error maps at multiple scales and thus improve the residual learning efficiency. Extensive experiments on the Scene Flow and KITTI datasets show that EDNet outperforms the previous 3D CNN based works and achieves state-of-the-art performance with significantly faster speed and less memory consumption. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_EDNet_Efficient_Disparity_Estimation_With_Cost_Volume_Combination_and_Attention-Based_CVPR_2021_paper.pdf | http://arxiv.org/abs/2010.13338 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_EDNet_Efficient_Disparity_Estimation_With_Cost_Volume_Combination_and_Attention-Based_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_EDNet_Efficient_Disparity_Estimation_With_Cost_Volume_Combination_and_Attention-Based_CVPR_2021_paper.html | CVPR 2021 | null | null |
Unsupervised Visual Representation Learning by Tracking Patches in Video | Guangting Wang, Yizhou Zhou, Chong Luo, Wenxuan Xie, Wenjun Zeng, Zhiwei Xiong | Inspired by the fact that human eyes continue to develop tracking ability in early and middle childhood, we propose to use tracking as a proxy task for a computer vision system to learn the visual representations. Modelled on the Catch game played by the children, we design a Catch-the-Patch (CtP) game for a 3D-CNN model to learn visual representations that would help with video-related tasks. In the proposed pretraining framework, we cut an image patch from a given video and let it scale and move according to a pre-set trajectory. The proxy task is to estimate the position and size of the image patch in a sequence of video frames, given only the target bounding box in the first frame. We discover that using multiple image patches simultaneously brings clear benefits. We further increase the difficulty of the game by randomly making patches invisible. Extensive experiments on mainstream benchmarks demonstrate the superior performance of CtP against other video pretraining methods. In addition, CtP-pretrained features are less sensitive to domain gaps than those trained by a supervised action recognition task. When both trained on Kinetics-400, we are pleasantly surprised to find that CtP-pretrained representation achieves much higher action classification accuracy than its fully supervised counterpart on Something-Something dataset. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Unsupervised_Visual_Representation_Learning_by_Tracking_Patches_in_Video_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.02545 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Unsupervised_Visual_Representation_Learning_by_Tracking_Patches_in_Video_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Unsupervised_Visual_Representation_Learning_by_Tracking_Patches_in_Video_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Unsupervised_Visual_Representation_CVPR_2021_supplemental.pdf | null |
Wasserstein Contrastive Representation Distillation | Liqun Chen, Dong Wang, Zhe Gan, Jingjing Liu, Ricardo Henao, Lawrence Carin | The primary goal of knowledge distillation (KD) is to encapsulate the information of a model learned from a teacher network into a student network, with the latter being more compact than the former. Existing work, e.g., using Kullback-Leibler divergence for distillation, may fail to capture important structural knowledge in the teacher network and often lacks the ability for feature generalization, particularly in situations when teacher and student are built to address different classification tasks. We propose Wasserstein Contrastive Representation Distillation (WCoRD), which leverages both primal and dual forms of Wasserstein distance for KD. The dual form is used for global knowledge transfer, yielding a contrastive learning objective that maximizes the lower bound of mutual information between the teacher and the student networks. The primal form is used for local contrastive knowledge transfer within a mini-batch, effectively matching the distributions of features between the teacher and the student networks. Experiments demonstrate that the proposed WCoRD method outperforms state-of-the-art approaches on privileged information distillation, model compression and cross-modal transfer. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Wasserstein_Contrastive_Representation_Distillation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.08674 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Wasserstein_Contrastive_Representation_Distillation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Wasserstein_Contrastive_Representation_Distillation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Wasserstein_Contrastive_Representation_CVPR_2021_supplemental.pdf | null |
Learnable Companding Quantization for Accurate Low-Bit Neural Networks | Kohei Yamamoto | Quantizing deep neural networks is an effective method for reducing memory consumption and improving inference speed, and is thus useful for implementation in resource-constrained devices. However, it is still hard for extremely low-bit models to achieve accuracy comparable with that of full-precision models. To address this issue, we propose learnable companding quantization (LCQ) as a novel non-uniform quantization method for 2-, 3-, and 4-bit models. LCQ jointly optimizes model weights and learnable companding functions that can flexibly and non-uniformly control the quantization levels of weights and activations. We also present a new weight normalization technique that allows more stable training for quantization. Experimental results show that LCQ outperforms conventional state-of-the-art methods and narrows the gap between quantized and full-precision models for image classification and object detection tasks. Notably, the 2-bit ResNet-50 model on ImageNet achieves top-1 accuracy of 75.1% and reduces the gap to 1.7%, allowing LCQ to further exploit the potential of non-uniform quantization. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yamamoto_Learnable_Companding_Quantization_for_Accurate_Low-Bit_Neural_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07156 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yamamoto_Learnable_Companding_Quantization_for_Accurate_Low-Bit_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yamamoto_Learnable_Companding_Quantization_for_Accurate_Low-Bit_Neural_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yamamoto_Learnable_Companding_Quantization_CVPR_2021_supplemental.pdf | null |
FaceInpainter: High Fidelity Face Adaptation to Heterogeneous Domains | Jia Li, Zhaoyang Li, Jie Cao, Xingguang Song, Ran He | In this work, we propose a novel two-stage framework named FaceInpainter to implement controllable Identity-Guided Face Inpainting (IGFI) under heterogeneous domains. Concretely, by explicitly disentangling foreground and background of the target face, the first stage focuses on adaptive face fitting to the fixed background via a Styled Face Inpainting Network (SFI-Net), with 3D priors and texture code of the target, as well as identity factor of the source face. It is challenging to deal with the inconsistency between the new identity of the source and the original background of the target, concerning the face shape and appearance on the fused boundary. The second stage consists of a Joint Refinement Network (JR-Net) to refine the swapped face. It leverages AdaIN considering identity and multi-scale texture codes, for feature transformation of the decoded face from SFI-Net with facial occlusions. We adopt the contextual loss to implicitly preserve the attributes, encouraging face deformation and fewer texture distortions. Experimental results demonstrate that our approach handles high-quality identity adaptation to heterogeneous domains, exhibiting the competitive performance compared with state-of-the-art methods concerning both attribute and identity fidelity. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_FaceInpainter_High_Fidelity_Face_Adaptation_to_Heterogeneous_Domains_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_FaceInpainter_High_Fidelity_Face_Adaptation_to_Heterogeneous_Domains_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_FaceInpainter_High_Fidelity_Face_Adaptation_to_Heterogeneous_Domains_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_FaceInpainter_High_Fidelity_CVPR_2021_supplemental.pdf | null |
How Robust Are Randomized Smoothing Based Defenses to Data Poisoning? | Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm | Predictions of certifiably robust classifiers remain constant in a neighborhood of a point, making them resilient to test-time attacks with a guarantee. In this work, we present a previously unrecognized threat to robust machine learning models that highlights the importance of training-data quality in achieving high certified adversarial robustness. Specifically, we propose a novel bilevel optimization-based data poisoning attack that degrades the robustness guarantees of certifiably robust classifiers. Unlike other poisoning attacks that reduce the accuracy of the poisoned models on a small set of target points, our attack reduces the average certified radius (ACR) of an entire target class in the dataset. Moreover, our attack is effective even when the victim trains the models from scratch using state-of-the-art robust training methods such as Gaussian data augmentation [??], MACER [??], and SmoothAdv [??] that achieve high certified adversarial robustness. To make the attack harder to detect, we use clean-label poisoning points with imperceptible distortions. The effectiveness of the proposed method is evaluated by poisoning MNIST and CIFAR10 datasets and training deep neural networks using previously mentioned training methods and certifying the robustness with randomized smoothing. The ACR of the target class, for models trained on generated poison data, can be reduced by more than 30%. Moreover, the poisoned data is transferable to models trained with different training methods and models with different architectures. | https://openaccess.thecvf.com/content/CVPR2021/papers/Mehra_How_Robust_Are_Randomized_Smoothing_Based_Defenses_to_Data_Poisoning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.01274 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Mehra_How_Robust_Are_Randomized_Smoothing_Based_Defenses_to_Data_Poisoning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Mehra_How_Robust_Are_Randomized_Smoothing_Based_Defenses_to_Data_Poisoning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mehra_How_Robust_Are_CVPR_2021_supplemental.pdf | null |
Deep Learning in Latent Space for Video Prediction and Compression | Bowen Liu, Yu Chen, Shiyu Liu, Hun-Seok Kim | Learning-based video compression has achieved substantial progress during recent years. The most influential approaches adopt deep neural networks (DNNs) to remove spatial and temporal redundancies by finding the appropriate lower-dimensional representations of frames in the video. We propose a novel DNN based framework that predicts and compresses video sequences in the latent vector space. The proposed method first learns the efficient lower-dimensional latent space representation of each video frame and then performs inter-frame prediction in that latent domain. The proposed latent domain compression of individual frames is obtained by a deep autoencoder trained with a generative adversarial network (GAN). To exploit the temporal correlation within the video frame sequence, we employ a convolutional long short-term memory (ConvLSTM) network to predict the latent vector representation of the future frame. We demonstrate our method with two applications; video compression and abnormal event detection that share the identical latent frame prediction network. The proposed method exhibits superior or competitive performance compared to the state-of-the-art algorithms specifically designed for either video compression or anomaly detection. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Deep_Learning_in_Latent_Space_for_Video_Prediction_and_Compression_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Deep_Learning_in_Latent_Space_for_Video_Prediction_and_Compression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Deep_Learning_in_Latent_Space_for_Video_Prediction_and_Compression_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Deep_Learning_in_CVPR_2021_supplemental.pdf | null |
PWCLO-Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical Embedding Mask Optimization | Guangming Wang, Xinrui Wu, Zhe Liu, Hesheng Wang | A novel 3D point cloud learning model for deep LiDAR odometry, named PWCLO-Net, using hierarchical embedding mask optimization is proposed in this paper. In this model, the Pyramid, Warping, and Cost volume (PWC) structure for the LiDAR odometry task is built to refine the estimated pose in a coarse-to-fine approach hierarchically. An attentive cost volume is built to associate two point clouds and obtain embedding motion patterns. Then, a novel trainable embedding mask is proposed to weigh the local motion patterns of all points to regress the overall pose and filter outlier points. The estimated current pose is used to warp the first point cloud to bridge the distance to the second point cloud, and then the cost volume of the residual motion is built. At the same time, the embedding mask is optimized hierarchically from coarse to fine to obtain more accurate filtering information for pose refinement. The trainable pose warp-refinement process is iteratively used to make the pose estimation more robust for outliers. The superior performance and effectiveness of our LiDAR odometry model are demonstrated on KITTI odometry dataset. Our method outperforms all recent learning-based methods and outperforms the geometry-based approach, LOAM with mapping optimization, on most sequences of KITTI odometry dataset. Our source codes will be released on https://github.com/IRMVLab/PWCLONet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_PWCLO-Net_Deep_LiDAR_Odometry_in_3D_Point_Clouds_Using_Hierarchical_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_PWCLO-Net_Deep_LiDAR_Odometry_in_3D_Point_Clouds_Using_Hierarchical_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_PWCLO-Net_Deep_LiDAR_Odometry_in_3D_Point_Clouds_Using_Hierarchical_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_PWCLO-Net_Deep_LiDAR_CVPR_2021_supplemental.zip | null |
ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-Supervised Continual Learning | Liyuan Wang, Kuo Yang, Chongxuan Li, Lanqing Hong, Zhenguo Li, Jun Zhu | Continual learning usually assumes the incoming data are fully labeled, which might not be applicable in real applications. In this work, we consider semi-supervised continual learning (SSCL) that incrementally learns from partially labeled data. Observing that existing continual learning methods lack the ability to continually exploit the unlabeled data, we propose deep Online Replay with Discriminator Consistency (ORDisCo) to interdependently learn a classifier with a conditional generative adversarial network (GAN), which continually passes the learned data distribution to the classifier. In particular, ORDisCo replays data sampled from the conditional generator to the classifier in an online manner, exploiting unlabeled data in a time- and storage-efficient way. Further, to explicitly overcome the catastrophic forgetting of unlabeled data, we selectively stabilize parameters of the discriminator that are important for discriminating the pairs of old unlabeled data and their pseudo-labels predicted by the classifier. We extensively evaluate ORDisCo on various semi-supervised learning benchmark datasets for SSCL, and show that ORDisCo achieves significant performance improvement on SVHN, CIFAR10 and Tiny-ImageNet, compared to strong baselines. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_ORDisCo_Effective_and_Efficient_Usage_of_Incremental_Unlabeled_Data_for_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.00407 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_ORDisCo_Effective_and_Efficient_Usage_of_Incremental_Unlabeled_Data_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_ORDisCo_Effective_and_Efficient_Usage_of_Incremental_Unlabeled_Data_for_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_ORDisCo_Effective_and_CVPR_2021_supplemental.pdf | null |
Dynamic Region-Aware Convolution | Jin Chen, Xijun Wang, Zichao Guo, Xiangyu Zhang, Jian Sun | We propose a new convolution called Dynamic Region-Aware Convolution (DRConv), which can automatically assign multiple filters to corresponding spatial regions where features have similar representation. In this way, DRConv outperforms standard convolution in modeling semantic variations. Standard convolutional layer can increase the number of filers to extract more visual elements but results in high computational cost. More gracefully, our DRConv transfers the increasing channel-wise filters to spatial dimension with learnable instructor, which not only improve representation ability of convolution, but also maintains computational cost and the translation-invariance as standard convolution dose. DRConv is an effective and elegant method for handling complex and variable spatial information distribution. It can substitute standard convolution in any existing networks for its plug-and-play property, especially to power convolution layers in efficient networks. We evaluate DRConv on a wide range of models (MobileNet series, ShuffleNetV2, etc.) and tasks (Classification, Face Recognition, Detection and Segmentation). On ImageNet classification, DRConv-based ShuffleNetV2-0.5x achieves state-of-the-art performance of 67.1% at 46M multiply-adds level with 6.3% relative improvement. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Dynamic_Region-Aware_Convolution_CVPR_2021_paper.pdf | http://arxiv.org/abs/2003.12243 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Dynamic_Region-Aware_Convolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Dynamic_Region-Aware_Convolution_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Dynamic_Region-Aware_Convolution_CVPR_2021_supplemental.pdf | null |
Explore Image Deblurring via Encoded Blur Kernel Space | Phong Tran, Anh Tuan Tran, Quynh Phung, Minh Hoai | This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space. Assuming the encoded kernel space is close enough to in-the-wild blur operators, we propose an alternating optimization algorithm for blind image deblurring. It approximates an unseen blur operator by a kernel in the encoded space and searches for the corresponding sharp image. Unlike recent deep-learning-based methods, our system can handle unseen blur kernel, while avoiding using complicated handcrafted priors on the blur operator often found in classical methods. Due to the method's design, the encoded kernel space is fully differentiable, thus can be easily adopted in deep neural network models. Moreover, our method can be used for blur synthesis by transferring existing blur operators from a given dataset into a new domain. Finally, we provide experimental results to confirm the effectiveness of the proposed method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tran_Explore_Image_Deblurring_via_Encoded_Blur_Kernel_Space_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tran_Explore_Image_Deblurring_via_Encoded_Blur_Kernel_Space_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tran_Explore_Image_Deblurring_via_Encoded_Blur_Kernel_Space_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tran_Explore_Image_Deblurring_CVPR_2021_supplemental.pdf | null |
BCNet: Searching for Network Width With Bilaterally Coupled Network | Xiu Su, Shan You, Fei Wang, Chen Qian, Changshui Zhang, Chang Xu | Searching for a more compact network width recently serves as an effective way of channel pruning for the deployment of convolutional neural networks (CNNs) under hardware constraints. To fulfill the searching, a one-shot supernet is usually leveraged to efficiently evaluate the performance \wrt different network widths. However, current methods mainly follow a unilaterally augmented (UA) principle for the evaluation of each width, which induces the training unfairness of channels in supernet. In this paper, we introduce a new supernet called Bilaterally Coupled Network (BCNet) to address this issue. In BCNet, each channel is fairly trained and responsible for the same amount of network widths, thus each network width can be evaluated more accurately. Besides, we leverage a stochastic complementary strategy for training the BCNet, and propose a prior initial population sampling method to boost the performance of the evolutionary search. Extensive experiments on benchmark CIFAR-10 and ImageNet datasets indicate that our method can achieve state-of-the-art or competing performance over other baseline methods. Moreover, our method turns out to further boost the performance of NAS models by refining their network widths. For example, with the same FLOPs budget, our obtained EfficientNet-B0 achieves 77.36% Top-1 accuracy on ImageNet dataset, surpassing the performance of original setting by 0.48%. | https://openaccess.thecvf.com/content/CVPR2021/papers/Su_BCNet_Searching_for_Network_Width_With_Bilaterally_Coupled_Network_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.10533 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Su_BCNet_Searching_for_Network_Width_With_Bilaterally_Coupled_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Su_BCNet_Searching_for_Network_Width_With_Bilaterally_Coupled_Network_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Su_BCNet_Searching_for_CVPR_2021_supplemental.pdf | null |
Camera Pose Matters: Improving Depth Prediction by Mitigating Pose Distribution Bias | Yunhan Zhao, Shu Kong, Charless Fowlkes | Monocular depth predictors are typically trained on large-scale training sets which are naturally biased w.r.t the distribution of camera poses. As a result, trained predictors fail to make reliable depth predictions for testing examples captured under uncommon camera poses. To address this issue, we propose two novel techniques that exploit the camera pose during training and prediction. First, we introduce a simple perspective-aware data augmentation that synthesizes new training examples with more diverse views by perturbing the existing ones in a geometrically consistent manner. Second, we propose a conditional model that exploits the per-image camera pose as prior knowledge by encoding it as a part of the input. We show that jointly applying the two methods improves depth prediction on images captured under uncommon and even never-before-seen camera poses. We show that our methods improve performance when applied to a range of different predictor architectures. Lastly, we show that explicitly encoding the camera pose distribution improves the generalization performance of a synthetically trained depth predictor when evaluated on real images. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Camera_Pose_Matters_Improving_Depth_Prediction_by_Mitigating_Pose_Distribution_CVPR_2021_paper.pdf | http://arxiv.org/abs/2007.03887 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Camera_Pose_Matters_Improving_Depth_Prediction_by_Mitigating_Pose_Distribution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Camera_Pose_Matters_Improving_Depth_Prediction_by_Mitigating_Pose_Distribution_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhao_Camera_Pose_Matters_CVPR_2021_supplemental.pdf | null |
Lipstick Ain't Enough: Beyond Color Matching for In-the-Wild Makeup Transfer | Thao Nguyen, Anh Tuan Tran, Minh Hoai | Makeup transfer is the task of applying on a source face the makeup style from a reference image. Real-life makeups are diverse and wild, which cover not only color-changing but also patterns, such as stickers, blushes, and jewelries. However, existing works overlooked the latter components and confined makeup transfer to color manipulation, focusing only on light makeup styles. In this work, we propose a holistic makeup transfer framework that can handle all the mentioned makeup components. It consists of an improved color transfer branch and a novel pattern transfer branch to learn all makeup properties, including color, shape, texture, and location. To train and evaluate such a system, we also introduce new makeup datasets for real and synthetic extreme makeup. Experimental results show that our framework achieves the state of the art performance on both light and extreme makeup styles. | https://openaccess.thecvf.com/content/CVPR2021/papers/Nguyen_Lipstick_Aint_Enough_Beyond_Color_Matching_for_In-the-Wild_Makeup_Transfer_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Nguyen_Lipstick_Aint_Enough_Beyond_Color_Matching_for_In-the-Wild_Makeup_Transfer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Nguyen_Lipstick_Aint_Enough_Beyond_Color_Matching_for_In-the-Wild_Makeup_Transfer_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Nguyen_Lipstick_Aint_Enough_CVPR_2021_supplemental.pdf | null |
Generative Interventions for Causal Learning | Chengzhi Mao, Augustine Cha, Amogh Gupta, Hao Wang, Junfeng Yang, Carl Vondrick | We introduce a framework for learning robust visual representations that generalize to new viewpoints, backgrounds, and scene contexts. Discriminative models often learn naturally occurring spurious correlations, which cause them to fail on images outside of the training distribution. In this paper, we show that we can steer generative models to manufacture interventions on features caused by confounding factors. Experiments, visualizations, and theoretical results show this method learns robust representations more consistent with the underlying causal relationships. Our approach improves performance on multiple datasets demanding out-of-distribution generalization, and we demonstrate state-of-the-art performance generalizing from ImageNet to ObjectNet dataset. | https://openaccess.thecvf.com/content/CVPR2021/papers/Mao_Generative_Interventions_for_Causal_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.12265 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Mao_Generative_Interventions_for_Causal_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Mao_Generative_Interventions_for_Causal_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
Graph Stacked Hourglass Networks for 3D Human Pose Estimation | Tianhan Xu, Wataru Takano | In this paper, we propose a novel graph convolutional network architecture, Graph Stacked Hourglass Networks, for 2D-to-3D human pose estimation tasks. The proposed architecture consists of repeated encoder-decoder, in which graph-structured features are processed across three different scales of human skeletal representations. This multi-scale architecture enables the model to learn both local and global feature representations, which are critical for 3D human pose estimation. We also introduce a multi-level feature learning approach using different-depth intermediate features and show the performance improvements that result from exploiting multi-scale, multi-level feature representations. Extensive experiments are conducted to validate our approach, and the results show that our model outperforms the state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Graph_Stacked_Hourglass_Networks_for_3D_Human_Pose_Estimation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16385 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Graph_Stacked_Hourglass_Networks_for_3D_Human_Pose_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Graph_Stacked_Hourglass_Networks_for_3D_Human_Pose_Estimation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Adaptive Aggregation Networks for Class-Incremental Learning | Yaoyao Liu, Bernt Schiele, Qianru Sun | Class-Incremental Learning (CIL) aims to learn a classification model with the number of classes increasing phase-by-phase. An inherent problem in CIL is the stability-plasticity dilemma between the learning of old and new classes, i.e., high-plasticity models easily forget old classes, but high-stability models are weak to learn new classes. We alleviate this issue by proposing a novel network architecture called Adaptive Aggregation Networks (AANets), in which we explicitly build two types of residual blocks at each residual level (taking ResNet as the baseline architecture): a stable block and a plastic block. We aggregate the output feature maps from these two blocks and then feed the results to the next-level blocks. We adapt the aggregation weights in order to balance these two types of blocks, i.e., to balance stability and plasticity, dynamically. We conduct extensive experiments on three CIL benchmarks: CIFAR-100, ImageNet-Subset, and ImageNet, and show that many existing CIL methods can be straightforwardly incorporated into the architecture of AANets to boost their performances. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Adaptive_Aggregation_Networks_for_Class-Incremental_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2010.05063 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Adaptive_Aggregation_Networks_for_Class-Incremental_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Adaptive_Aggregation_Networks_for_Class-Incremental_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Adaptive_Aggregation_Networks_CVPR_2021_supplemental.pdf | null |
VS-Net: Voting With Segmentation for Visual Localization | Zhaoyang Huang, Han Zhou, Yijin Li, Bangbang Yang, Yan Xu, Xiaowei Zhou, Hujun Bao, Guofeng Zhang, Hongsheng Li | Visual localization is of great importance in robotics and computer vision. Recently, scene coordinate regression based methods have shown good performance in visual localization in small static scenes. However, it still estimates camera poses from many inferior scene coordinates. To address this problem, we propose a novel visual localization framework that establishes 2D-to-3D correspondences between the query image and the 3D map with a series of learnable scene-specific landmarks. In the landmark generation stage, the 3D surfaces of the target scene are over-segmented into mosaic patches whose centers are regarded as the scene-specific landmarks. To robustly and accurately recover the scene-specific landmarks, we propose the Voting with Segmentation Network (VS-Net) to segment the pixels into different landmark patches with a segmentation branch and estimate the landmark locations within each patch with a landmark location voting branch. Since the number of landmarks in a scene may reach up to 5000, training a segmentation network with such a large number of classes is both computation and memory costly for the commonly used cross-entropy loss. We propose a novel prototype-based triplet loss with hard negative mining, which is able to train semantic segmentation networks with a large number of labels efficiently. Our proposed VS-Net is extensively tested on multiple public benchmarks and can outperform state-of-the-art visual localization methods. Code and models are available at https://github.com/zju3dv/VS-Net. | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_VS-Net_Voting_With_Segmentation_for_Visual_Localization_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_VS-Net_Voting_With_Segmentation_for_Visual_Localization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_VS-Net_Voting_With_Segmentation_for_Visual_Localization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_VS-Net_Voting_With_CVPR_2021_supplemental.pdf | null |
Learning To Identify Correct 2D-2D Line Correspondences on Sphere | Haoang Li, Kai Chen, Ji Zhao, Jiangliu Wang, Pyojin Kim, Zhe Liu, Yun-Hui Liu | Given a set of putative 2D-2D line correspondences, we aim to identify correct matches. Existing methods exploit the geometric constraints. They are only applicable to structured scenes with orthogonality, parallelism and coplanarity. In contrast, we propose the first approach suitable for both structured and unstructured scenes. Instead of geometric constraint, we leverage the spatial regularity on sphere. Specifically, we propose to map line correspondences into vectors tangent to sphere. We use these vectors to encode both angular and positional variations of image lines, which is more reliable and concise than directly using inclinations, midpoints or endpoints of image lines. Neighboring vectors mapped from correct matches exhibit a spatial regularity called local trend consistency, regardless of the type of scenes. To encode this regularity, we design a neural network and also propose a novel loss function that enforces the smoothness constraint of vector field. In addition, we establish a large real-world dataset for image line matching. Experiments showed that our approach outperforms state-of-the-art ones in terms of accuracy, efficiency and robustness, and also leads to high generalization. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Learning_To_Identify_Correct_2D-2D_Line_Correspondences_on_Sphere_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Learning_To_Identify_Correct_2D-2D_Line_Correspondences_on_Sphere_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Learning_To_Identify_Correct_2D-2D_Line_Correspondences_on_Sphere_CVPR_2021_paper.html | CVPR 2021 | null | null |
Domain-Independent Dominance of Adaptive Methods | Pedro Savarese, David McAllester, Sudarshan Babu, Michael Maire | From a simplified analysis of adaptive methods, we derive AvaGrad, a new optimizer which outperforms SGD on vision tasks when its adaptability is properly tuned. We observe that the power of our method is partially explained by a decoupling of learning rate and adaptability, greatly simplifying hyperparameter search. In light of this observation, we demonstrate that, against conventional wisdom, Adam can also outperform SGD on vision tasks, as long as the coupling between its learning rate and adaptability is taken into account. In practice, AvaGrad matches the best results, as measured by generalization accuracy, delivered by any existing optimizer (SGD or adaptive) across image classification (CIFAR, ImageNet) and character-level language modelling (Penn Treebank) tasks. When training GANs, AvaGrad improves upon existing optimizers. | https://openaccess.thecvf.com/content/CVPR2021/papers/Savarese_Domain-Independent_Dominance_of_Adaptive_Methods_CVPR_2021_paper.pdf | http://arxiv.org/abs/1912.01823 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Savarese_Domain-Independent_Dominance_of_Adaptive_Methods_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Savarese_Domain-Independent_Dominance_of_Adaptive_Methods_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Savarese_Domain-Independent_Dominance_of_CVPR_2021_supplemental.pdf | null |
What if We Only Use Real Datasets for Scene Text Recognition? Toward Scene Text Recognition With Fewer Labels | Jeonghun Baek, Yusuke Matsui, Kiyoharu Aizawa | Scene text recognition (STR) task has a common practice: All state-of-the-art STR models are trained on large synthetic data. In contrast to this practice, training STR models only on fewer real labels (STR with fewer labels) is important when we have to train STR models without synthetic data: for handwritten or artistic texts that are difficult to generate synthetically and for languages other than English for which we do not always have synthetic data. However, there has been implicit common knowledge that training STR models on real data is nearly impossible because real data is insufficient. We consider that this common knowledge has obstructed the study of STR with fewer labels. In this work, we would like to reactivate STR with fewer labels by disproving the common knowledge. We consolidate recently accumulated public real data and show that we can train STR models satisfactorily only with real labeled data. Subsequently, we find simple data augmentation to fully exploit real data. Furthermore, we improve the models by collecting unlabeled data and introducing semi- and self-supervised methods. As a result, we obtain a competitive model to state-of-the-art methods. To the best of our knowledge, this is the first study that 1) shows sufficient performance by only using real labels and 2) introduces semi- and self-supervised methods into STR with fewer labels. Our code and data are available. | https://openaccess.thecvf.com/content/CVPR2021/papers/Baek_What_if_We_Only_Use_Real_Datasets_for_Scene_Text_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.04400 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Baek_What_if_We_Only_Use_Real_Datasets_for_Scene_Text_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Baek_What_if_We_Only_Use_Real_Datasets_for_Scene_Text_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Baek_What_if_We_CVPR_2021_supplemental.pdf | null |
Incremental Learning via Rate Reduction | Ziyang Wu, Christina Baek, Chong You, Yi Ma | Current deep learning architectures suffer from catastrophic forgetting, a failure to retain knowledge of previously learned classes when incrementally trained on new classes. The fundamental roadblock faced by deep learning methods is that the models are optimized as "black boxes", making it difficult to properly adjust the model parameters to preserve knowledge about previously seen data. To overcome the problem of catastrophic forgetting, we propose utilizing an alternative "white box" architecture derived from the principle of rate reduction, where each layer of the network is explicitly computed without back propagation. Under this paradigm, we demonstrate that, given a pretrained network and new data classes, our approach can provably construct a new network that emulates joint training with all past and new classes. Finally, our experiments show that our proposed learning algorithm observes significantly less decay in classification performance, outperforming state of the art methods on MNIST and CIFAR-10 by a large margin and justifying the use of "white box" algorithms for incremental learning even for sufficiently complex image data. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Incremental_Learning_via_Rate_Reduction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.14593 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Incremental_Learning_via_Rate_Reduction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Incremental_Learning_via_Rate_Reduction_CVPR_2021_paper.html | CVPR 2021 | null | null |
Neural Descent for Visual 3D Human Pose and Shape | Andrei Zanfir, Eduard Gabriel Bazavan, Mihai Zanfir, William T. Freeman, Rahul Sukthankar, Cristian Sminchisescu | We present deep neural network methodology to reconstruct the 3d pose and shape of people, including hand gestures and facial expression, given an input RGB image. We rely on a recently introduced, expressive full body statistical 3d human model, GHUM, trained end-to-end, and learn to reconstruct its pose and shape state in a self-supervised regime. Central to our methodology, is a learning to learn and optimize approach, referred to as HUman Neural Descent (HUND), which avoids both second-order differentiation when training the model parameters, and expensive state gradient descen tin order to accurately minimize a semantic differentiable rendering loss at test time. Instead, we rely on novel recurrent stages to update the pose and shape parameters such that not only losses are minimized effectively, but the process is meta-regularized in order to ensure end-progress. HUND's symmetry between training and testing makes it the first 3d human sensing architecture to natively support different operating regimes including self-supervised ones. In diverse tests, we show that HUND achieves very competitive results in datasets like H3.6M and 3DPW, as well as good quality 3d reconstructions for complex imagery collected in-the-wild. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zanfir_Neural_Descent_for_Visual_3D_Human_Pose_and_Shape_CVPR_2021_paper.pdf | http://arxiv.org/abs/2008.06910 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zanfir_Neural_Descent_for_Visual_3D_Human_Pose_and_Shape_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zanfir_Neural_Descent_for_Visual_3D_Human_Pose_and_Shape_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zanfir_Neural_Descent_for_CVPR_2021_supplemental.zip | null |
HR-NAS: Searching Efficient High-Resolution Neural Architectures With Lightweight Transformers | Mingyu Ding, Xiaochen Lian, Linjie Yang, Peng Wang, Xiaojie Jin, Zhiwu Lu, Ping Luo | High-resolution representations (HR) are essential for dense prediction tasks such as segmentation, detection, and pose estimation. Learning HR representations is typically ignored in previous Neural Architecture Search (NAS) methods that focus on image classification. This work proposes a novel NAS method, called HR-NAS, which is able to find efficient and accurate networks for different tasks, by effectively encoding multiscale contextual information while maintaining high-resolution representations. In HR-NAS, we renovate the NAS search space as well as its searching strategy. To better encode multiscale image contexts in the search space of HR-NAS, we first carefully design a lightweight transformer, whose computational complexity can be dynamically changed with respect to different objective functions and computation budgets. To maintain high-resolution representations of the learned networks, HR-NAS adopts a multi-branch architecture that provides convolutional encoding of multiple feature resolutions, inspired by HRNet. Last, we proposed an efficient fine-grained search strategy to train HR-NAS, which effectively explores the search space, and finds optimal architectures given various tasks and computation resources. HR-NAS is capable of achieving state-of-the-art trade-offs between performance and FLOPs for three dense prediction tasks and an image classification task, given only small computational budgets. For example, HR-NAS surpasses SqueezeNAS that is specially designed for semantic segmentation by a large margin of 3.61% while improving efficiency by 45.9%. Code is available at https://github.com/dingmyu/HR-NAS. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ding_HR-NAS_Searching_Efficient_High-Resolution_Neural_Architectures_With_Lightweight_Transformers_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ding_HR-NAS_Searching_Efficient_High-Resolution_Neural_Architectures_With_Lightweight_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ding_HR-NAS_Searching_Efficient_High-Resolution_Neural_Architectures_With_Lightweight_Transformers_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ding_HR-NAS_Searching_Efficient_CVPR_2021_supplemental.pdf | null |
Transitional Adaptation of Pretrained Models for Visual Storytelling | Youngjae Yu, Jiwan Chung, Heeseung Yun, Jongseok Kim, Gunhee Kim | Previous models for vision-to-language generation tasks usually pretrain a visual encoder and a language generator in the respective domains and jointly finetune them with the target task. However, this direct transfer practice may suffer from the discord between visual specificity and language fluency since they are often separately trained from large corpora of visual and text data with no common ground. In this work, we claim that a transitional adaptation task is required between pretraining and finetuning to harmonize the visual encoder and the language model for challenging downstream target tasks like visual storytelling. We propose a novel approach named Transitional Adaptation of Pretrained Model (TAPM) that adapts the multi-modal modules to each other with a simpler alignment task between visual inputs only with no need for text labels. Through extensive experiments, we show that the adaptation step significantly improves the performance of multiple language models for sequential video and image captioning tasks. We achieve new state-of-the-art performance on both language metrics and human evaluation in the multi-sentence description task of LSMDC 2019 and the image storytelling task of VIST. Our experiments reveal that this improvement in caption quality does not depend on the specific choice of language models. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yu_Transitional_Adaptation_of_Pretrained_Models_for_Visual_Storytelling_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_Transitional_Adaptation_of_Pretrained_Models_for_Visual_Storytelling_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_Transitional_Adaptation_of_Pretrained_Models_for_Visual_Storytelling_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yu_Transitional_Adaptation_of_CVPR_2021_supplemental.pdf | null |
Improving Panoptic Segmentation at All Scales | Lorenzo Porzi, Samuel Rota Bulo, Peter Kontschieder | Crop-based training strategies decouple training resolution from GPU memory consumption, allowing the use of large-capacity panoptic segmentation networks on multi-megapixel images. Using crops, however, can introduce a bias towards truncating or missing large objects. To address this, we propose a novel crop-aware bounding box regression loss (CABB loss), which promotes predictions to be consistent with the visible parts of the cropped objects, while not over-penalizing them for extending outside of the crop. We further introduce a novel data sampling and augmentation strategy which improves generalization across scales by counteracting the imbalanced distribution of object sizes. Combining these two contributions with a carefully designed, top-down panoptic segmentation architecture, we obtain new state-of-the-art results on the challenging Mapillary Vistas (MVD), Indian Driving and Cityscapes datasets, surpassing the previously best approach on MVD by +4.5% PQ and +5.2% mAP. | https://openaccess.thecvf.com/content/CVPR2021/papers/Porzi_Improving_Panoptic_Segmentation_at_All_Scales_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.07717 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Porzi_Improving_Panoptic_Segmentation_at_All_Scales_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Porzi_Improving_Panoptic_Segmentation_at_All_Scales_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Porzi_Improving_Panoptic_Segmentation_CVPR_2021_supplemental.pdf | null |
Model-Contrastive Federated Learning | Qinbin Li, Bingsheng He, Dawn Song | Federated learning enables multiple parties to collaboratively train a machine learning model without communicating their local data. A key challenge in federated learning is to handle the heterogeneity of local data distribution across parties. Although many studies have been proposed to address this challenge, we find that they fail to achieve high performance in image datasets with deep learning models. In this paper, we propose MOON: model-contrastive federated learning. MOON is a simple and effective federated learning framework. The key idea of MOON is to utilize the similarity between model representations to correct the local training of individual parties, i.e., conducting contrastive learning in model-level. Our extensive experiments show that MOON significantly outperforms the other state-of-the-art federated learning algorithms on various image classification tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Model-Contrastive_Federated_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16257 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Model-Contrastive_Federated_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Model-Contrastive_Federated_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Model-Contrastive_Federated_Learning_CVPR_2021_supplemental.pdf | null |
Scalability vs. Utility: Do We Have To Sacrifice One for the Other in Data Importance Quantification? | Ruoxi Jia, Fan Wu, Xuehui Sun, Jiacen Xu, David Dao, Bhavya Kailkhura, Ce Zhang, Bo Li, Dawn Song | Quantifying the importance of each training point to a learning task is a fundamental problem in machine learning and the estimated importance scores have been leveraged to guide a range of data workflows such as data summarization and domain adaption. One simple idea is to use the leave-one-out error of each training point to indicate its importance. Recent work has also proposed to use the Shapley value, as it defines a unique value distribution scheme that satisfies a set of appealing properties. However, calculating Shapley values is often expensive, which limits its applicability in real-world applications at scale. Multiple heuristics to improve the scalability of calculating Shapley values have been proposed recently, with the potential risk of compromising their utility in real-world applications. How well do existing data quantification methods perform on existing workflows? How do these methods compare with each other, empirically and theoretically? Must we sacrifice scalability for the utility in these workflows when using these methods? In this paper, we conduct a novel theoretical analysis comparing the utility of different importance quantification methods, and report extensive experimental studies on settings such as noisy label detection, watermark removal, data summarization, data acquisition, and domain adaptation on existing and proposed workflows. We show that Shapley value approximation based on a KNN surrogate over pre-trained feature embeddings obtains comparable utility with existing algorithms while achieving significant scalability improvement, often by orders of magnitude. Our theoretical analysis also justifies its advantage over the leave-one-out error. The code is available at https://github.com/AI-secure/Shapley-Study. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jia_Scalability_vs._Utility_Do_We_Have_To_Sacrifice_One_for_CVPR_2021_paper.pdf | http://arxiv.org/abs/1911.07128 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jia_Scalability_vs._Utility_Do_We_Have_To_Sacrifice_One_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jia_Scalability_vs._Utility_Do_We_Have_To_Sacrifice_One_for_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jia_Scalability_vs._Utility_CVPR_2021_supplemental.pdf | null |
Hierarchical Layout-Aware Graph Convolutional Network for Unified Aesthetics Assessment | Dongyu She, Yu-Kun Lai, Gaoxiong Yi, Kun Xu | Learning computational models of image aesthetics can have a substantial impact on visual art and graphic design. Although automatic image aesthetics assessment is a challenging topic by its subjective nature, psychological studies have confirmed a strong correlation between image layouts and perceived image quality. While previous state-of-the-art methods attempt to learn holistic information using deep Convolutional Neural Networks (CNNs), our approach is motivated by the fact that Graph Convolutional Network (GCN) architecture is conceivably more suited for modeling complex relations among image regions than vanilla convolutional layers. Specifically, we present a Hierarchical Layout-Aware Graph Convolutional Network (HLA-GCN) to capture layout information. It is a dedicated double-subnet neural network consisting of two LAGCN modules. The first LA-GCN module constructs an aesthetics-related graph in the coordinate space and performs reasoning over spatial nodes. The second LA-GCN module performs graph reasoning after aggregating significant regions in a latent space. The model output is a hierarchical representation with layout-aware features from both spatial and aggregated nodes for unified aesthetics assessment. Extensive evaluations show that our proposed model outperforms the state-of-the-art on the AVA and AADB datasets across three different tasks. The code is available at http://github.com/days1011/HLAGCN. | https://openaccess.thecvf.com/content/CVPR2021/papers/She_Hierarchical_Layout-Aware_Graph_Convolutional_Network_for_Unified_Aesthetics_Assessment_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/She_Hierarchical_Layout-Aware_Graph_Convolutional_Network_for_Unified_Aesthetics_Assessment_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/She_Hierarchical_Layout-Aware_Graph_Convolutional_Network_for_Unified_Aesthetics_Assessment_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/She_Hierarchical_Layout-Aware_Graph_CVPR_2021_supplemental.pdf | null |
Normalized Avatar Synthesis Using StyleGAN and Perceptual Refinement | Huiwen Luo, Koki Nagano, Han-Wei Kung, Qingguo Xu, Zejian Wang, Lingyu Wei, Liwen Hu, Hao Li | We introduce a highly robust GAN-based framework for digitizing a normalized 3D avatar of a person from a single unconstrained photo. While the input image can be of a smiling person or taken in extreme lighting conditions, our method can reliably produce a high-quality textured model of a person's face in neutral expression and skin textures under diffuse lighting condition. Cutting-edge 3D face reconstruction methods use non-linear morphable face models combined with GAN-based decoders to capture the likeness and details of a person but fail to produce neutral head models with unshaded albedo textures which is critical for creating relightable and animation-friendly avatars for integration in virtual environments. The key challenges for existing methods to work is the lack of training and ground truth data containing normalized 3D faces. We propose a two-stage approach to address this problem. First, we adopt a highly robust normalized 3D face generator by embedding a non-linear morphable face model into a StyleGAN2 network. This allows us to generate detailed but normalized facial assets. This inference is then followed by a perceptual refinement step that uses the generated assets as regularization to cope with the limited available training samples of normalized faces. We further introduce a Normalized Face Dataset, which consists of a combination photogrammetry scans, carefully selected photographs, and generated fake people with neutral expressions in diffuse lighting conditions. While our prepared dataset contains two orders of magnitude less subjects than cutting edge GAN-based 3D facial reconstruction methods, we show that it is possible to produce high-quality normalized face models for very challenging unconstrained input images, and demonstrate superior performance to the current state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_Normalized_Avatar_Synthesis_Using_StyleGAN_and_Perceptual_Refinement_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.11423 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Normalized_Avatar_Synthesis_Using_StyleGAN_and_Perceptual_Refinement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Normalized_Avatar_Synthesis_Using_StyleGAN_and_Perceptual_Refinement_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Luo_Normalized_Avatar_Synthesis_CVPR_2021_supplemental.zip | null |
CT-Net: Complementary Transfering Network for Garment Transfer With Arbitrary Geometric Changes | Fan Yang, Guosheng Lin | Garment transfer shows great potential in realistic applications with the goal of transfering outfits across different people images. However, garment transfer between images with heavy misalignments or severe occlusions still remains as a challenge. In this work, we propose Complementary Transfering Network (CT-Net) to adaptively model different levels of geometric changes and transfer outfits between different people. In specific, CT-Net consists of three modules: i) A complementary warping module first estimates two complementary warpings to transfer the desired clothes in different granularities. ii) A layout prediction module is proposed to predict the target layout, which guides the preservation or generation of the body parts in the synthesized images. iii) A dynamic fusion module adaptively combines the advantages of the complementary warpings to render the garment transfer results. Extensive experiments conducted on DeepFashion dataset demonstrate that our network synthesizes high-quality garment transfer images and significantly outperforms the state-of-art methods both qualitatively and quantitatively. Our source code will be available online. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_CT-Net_Complementary_Transfering_Network_for_Garment_Transfer_With_Arbitrary_Geometric_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_CT-Net_Complementary_Transfering_Network_for_Garment_Transfer_With_Arbitrary_Geometric_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_CT-Net_Complementary_Transfering_Network_for_Garment_Transfer_With_Arbitrary_Geometric_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_CT-Net_Complementary_Transfering_CVPR_2021_supplemental.pdf | null |
MetaCorrection: Domain-Aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation | Xiaoqing Guo, Chen Yang, Baopu Li, Yixuan Yuan | Unsupervised domain adaptation (UDA) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain. Existing self-training based UDA approaches assign pseudo labels for target data and treat them as ground truth labels to fully leverage unlabeled target data for model adaptation. However, the generated pseudo labels from the model optimized on the source domain inevitably contain noise due to the domain gap. To tackle this issue, we advance a MetaCorrection framework, where a Domain-aware Meta-learning strategy is devised to benefit Loss Correction (DMLC) for UDA semantic segmentation. In particular, we model the noise distribution of pseudo labels in target domain by introducing a noise transition matrix (NTM) and construct meta data set with domain-invariant source data to guide the estimation of NTM. Through the risk minimization on the meta data set, the optimized NTM thus can correct the noisy issues in pseudo labels and enhance the generalization ability of the model on the target data. Considering the capacity gap between shallow and deep features, we further employ the proposed DMLC strategy to provide matched and compatible supervision signals for different level features, thereby ensuring deep adaptation. Extensive experimental results highlight the effectiveness of our method against existing state-of-the-art methods on three benchmarks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_MetaCorrection_Domain-Aware_Meta_Loss_Correction_for_Unsupervised_Domain_Adaptation_in_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05254 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_MetaCorrection_Domain-Aware_Meta_Loss_Correction_for_Unsupervised_Domain_Adaptation_in_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_MetaCorrection_Domain-Aware_Meta_Loss_Correction_for_Unsupervised_Domain_Adaptation_in_CVPR_2021_paper.html | CVPR 2021 | null | null |
Multi-Stage Progressive Image Restoration | Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, Ling Shao | Image restoration tasks demand a complex balance between spatial details and high-level contextualized information while recovering images. In this paper, we propose a novel synergistic design that can optimally balance these competing goals. Our main proposal is a multi-stage architecture, that progressively learns restoration functions for the degraded inputs, thereby breaking down the overall recovery process into more manageable steps. Specifically, our model first learns the contextualized features using encoder-decoder architectures and later combines them with a high-resolution branch that retains local information. At each stage, we introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features. A key ingredient in such a multi-stage architecture is the information exchange between different stages. To this end, we propose a two-faceted approach where the information is not only exchanged sequentially from early to late stages, but lateral connections between feature processing blocks also exist to avoid any loss of information. The resulting tightly interlinked multi-stage architecture, named as MPRNet, delivers strong performance gains on ten datasets across a range of tasks including image deraining, deblurring, and denoising. The source code and pre-trained models are available at https://github.com/swz30/MPRNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zamir_Multi-Stage_Progressive_Image_Restoration_CVPR_2021_paper.pdf | http://arxiv.org/abs/2102.02808 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zamir_Multi-Stage_Progressive_Image_Restoration_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zamir_Multi-Stage_Progressive_Image_Restoration_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zamir_Multi-Stage_Progressive_Image_CVPR_2021_supplemental.pdf | null |
PointNetLK Revisited | Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey | We address the generalization ability of recent learning-based point cloud registration methods. Despite their success, these approaches tend to have poor performance when applied to mismatched conditions that are not well-represented in the training set, such as unseen object categories, different complex scenes, or unknown depth sensors. In these circumstances, it has often been better to rely on classical non-learning methods (e.g., Iterative Closest Point), which have better generalization ability. Hybrid learning methods, that use learning for predicting point correspondences and then a deterministic step for alignment, have offered some respite, but are still limited in their generalization abilities. We revisit a recent innovation---PointNetLK---and show that the inclusion of an analytical Jacobian can exhibit remarkable generalization properties while reaping the inherent fidelity benefits of a learning framework. Our approach not only outperforms the state-of-the-art in mismatched conditions but also produces results competitive with current learning methods when operating on real-world test data close to the training set. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_PointNetLK_Revisited_CVPR_2021_paper.pdf | http://arxiv.org/abs/2008.09527 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_PointNetLK_Revisited_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_PointNetLK_Revisited_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_PointNetLK_Revisited_CVPR_2021_supplemental.pdf | null |
Deep Convolutional Dictionary Learning for Image Denoising | Hongyi Zheng, Hongwei Yong, Lei Zhang | Inspired by the great success of deep neural networks (DNNs), many unfolding methods have been proposed to integrate traditional image modeling techniques, such as dictionary learning (DicL) and sparse coding, into DNNs for image restoration. However, the performance of such methods remains limited for several reasons. First, the unfolded architectures do not strictly follow the image representation model of DicL and lose the desired physical meaning. Second, handcrafted priors are still used in most unfolding methods without effectively utilizing the learning capability of DNNs. Third, a universal dictionary is learned to represent all images, reducing the model representation flexibility. We propose a novel framework of deep convolutional dictionary learning (DCDicL), which follows the representation model of DicL strictly, learns the priors for both representation coefficients and the dictionaries, and can adaptively adjust the dictionary for each input image based on its content. The effectiveness of our DCDicL method is validated on the image denoising problem. DCDicL demonstrates leading denoising performance in terms of both quantitative metrics (e.g., PSNR, SSIM) and visual quality. In particular, it can reproduce the subtle image structures and textures, which are hard to recover by many existing denoising DNNs. The code is available at: https://github.com/natezhenghy/DCDicL_denoising. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Deep_Convolutional_Dictionary_Learning_for_Image_Denoising_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Deep_Convolutional_Dictionary_Learning_for_Image_Denoising_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Deep_Convolutional_Dictionary_Learning_for_Image_Denoising_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_Deep_Convolutional_Dictionary_CVPR_2021_supplemental.pdf | null |
Fourier Contour Embedding for Arbitrary-Shaped Text Detection | Yiqin Zhu, Jianyong Chen, Lingyu Liang, Zhanghui Kuang, Lianwen Jin, Wayne Zhang | One of the main challenges for arbitrary-shaped text detection is to design a good text instance representation that allows networks to learn diverse text geometry variances. Most of existing methods model text instances in image spatial domain via masks or contour point sequences in the Cartesian or the polar coordinate system. However, the mask representation might lead to expensive post-processing, while the point sequence one may have limited capability to model texts with highly-curved shapes. To tackle these problems, we model text instances in the Fourier domain and propose one novel Fourier Contour Embedding (FCE) method to represent arbitrary shaped text contours as compact signatures. We further construct FCENet with a backbone, feature pyramid networks (FPN) and a simple post-processing with the Inverse Fourier Transformation (IFT) and Non-Maximum Suppression (NMS). Different from previous methods, FCENet first predicts compact Fourier signatures of text instances, and then reconstructs text contours via IFT and NMS during test. Extensive experiments demonstrate that FCE is accurate and robust to fit contours of scene texts even with highly-curved shapes, and also validate the effectiveness and the good generalization of FCENet for arbitrary-shaped text detection. Furthermore, experimental results show that our FCENet is superior to the state-of-the-art (SOTA) methods on CTW1500 and Total-Text, especially on challenging highly-curved text subset. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Fourier_Contour_Embedding_for_Arbitrary-Shaped_Text_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.10442 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Fourier_Contour_Embedding_for_Arbitrary-Shaped_Text_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Fourier_Contour_Embedding_for_Arbitrary-Shaped_Text_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
TAP: Text-Aware Pre-Training for Text-VQA and Text-Caption | Zhengyuan Yang, Yijuan Lu, Jianfeng Wang, Xi Yin, Dinei Florencio, Lijuan Wang, Cha Zhang, Lei Zhang, Jiebo Luo | In this paper, we propose Text-Aware Pre-training (TAP) for Text-VQA and Text-Caption tasks. These two tasks aim at reading and understanding scene text in images for question answering and image caption generation, respectively. In contrast to the conventional vision-language pre-training that fails to capture scene text and its relationship with the visual and text modalities, TAP explicitly incorporates scene text (generated from OCR engines) in pre-training. With three pre-training tasks, including masked language modeling (MLM), image-text (contrastive) matching (ITM), and relative (spatial) position prediction (RPP), TAP effectively helps the model learn a better aligned representation among the three modalities: text word, visual object, and scene text. Due to this aligned representation learning, even pre-trained on the same downstream task dataset, TAP already boosts the absolute accuracy on the TextVQA dataset by +5.4%, compared with a non-TAP baseline. To further improve the performance, we build a large-scale dataset based on the Conceptual Caption dataset, named OCR-CC, which contains 1.4 million scene text-related image-text pairs. Pre-trained on this OCR-CC dataset, our approach outperforms the state of the art by large margins on multiple tasks, i.e., +8.3% accuracy on TextVQA, +8.6% accuracy on ST-VQA, and +10.2 CIDEr score on TextCaps. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_TAP_Text-Aware_Pre-Training_for_Text-VQA_and_Text-Caption_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.04638 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_TAP_Text-Aware_Pre-Training_for_Text-VQA_and_Text-Caption_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_TAP_Text-Aware_Pre-Training_for_Text-VQA_and_Text-Caption_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_TAP_Text-Aware_Pre-Training_CVPR_2021_supplemental.pdf | null |
Seeing Out of the Box: End-to-End Pre-Training for Vision-Language Representation Learning | Zhicheng Huang, Zhaoyang Zeng, Yupan Huang, Bei Liu, Dongmei Fu, Jianlong Fu | We study on joint learning of Convolutional Neural Network (CNN) and Transformer for vision-language pre-training (VLPT) which aims to learn cross-modal alignments from millions of image-text pairs. State-of-the-art approaches extract salient image regions and align regions with words step-by-step. As region-based representations usually represent parts of an image, it is challenging for existing models to fully understand the semantics from paired natural languages. In this paper, we propose SOHO to ""See Out of tHe bOx"" that takes a full image as input, and learns vision-language representation in an end-to-end manner. SOHO does not require bounding box annotations, while enables 10 times faster inference than region-based approaches. In particular, SOHO learns to extract comprehensive yet compact image features through a visual dictionary (VD) that facilitates cross-modal understanding. VD is designed to represent consistent visual abstractions of similar semantics, and VD can be further updated on-the-fly during pre-training. We conduct experiments on four well-established vision-language tasks by following standard VLPT settings. SOHO achieves absolute gains of 2.0% R@1 score on MSCOCO text retrieval 5k test split, 1.5% accuracy on NLVR2 test-P split, 6.7% accuracy on SNLI-VE test split, respectively. | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Seeing_Out_of_the_Box_End-to-End_Pre-Training_for_Vision-Language_Representation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03135 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Seeing_Out_of_the_Box_End-to-End_Pre-Training_for_Vision-Language_Representation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Seeing_Out_of_the_Box_End-to-End_Pre-Training_for_Vision-Language_Representation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_Seeing_Out_of_CVPR_2021_supplemental.pdf | null |
Quality-Agnostic Image Recognition via Invertible Decoder | Insoo Kim, Seungju Han, Ji-won Baek, Seong-Jin Park, Jae-Joon Han, Jinwoo Shin | Despite the remarkable performance of deep models on image recognition tasks, they are known to be susceptible to common corruptions such as blur, noise, and low-resolution. Data augmentation is a conventional way to build a robust model by considering these common corruptions during the training. However, a naive data augmentation scheme may result in a non-specialized model for particular corruptions, as the model tends to learn the averaged distribution among corruptions. To mitigate the issue, we propose a new paradigm of training deep image recognition networks that produce clean-like features from any quality image via an invertible neural architecture. The proposed method consists of two stages. In the first stage, we train an invertible network with only clean images under the recognition objective. In the second stage, its inversion, i.e., the invertible decoder, is attached to a new recognition network and we train this encoder-decoder network using both clean and corrupted images by considering recognition and reconstruction objectives. Our two-stage scheme allows the network to produce clean-like and robust features from any quality images, by reconstructing their clean images via the invertible decoder. We demonstrate the effectiveness of our method on image classification and face recognition tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_Quality-Agnostic_Image_Recognition_via_Invertible_Decoder_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Quality-Agnostic_Image_Recognition_via_Invertible_Decoder_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Quality-Agnostic_Image_Recognition_via_Invertible_Decoder_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_Quality-Agnostic_Image_Recognition_CVPR_2021_supplemental.pdf | null |
Hybrid Rotation Averaging: A Fast and Robust Rotation Averaging Approach | Yu Chen, Ji Zhao, Laurent Kneip | We address rotation averaging (RA) and its application to real-world 3D reconstruction. Local optimisation based approaches are the de facto choice, though they only guarantee a local optimum. Global optimisers ensure global optimality in low noise conditions, but they are inefficient and may easily deviate under the influence of outliers or elevated noise levels. We push the envelope of rotation averaging by leveraging the advantages of a global RA method and a local RA method. Combined with a fast view graph filtering as preprocessing, the proposed hybrid approach is robust to outliers. We further apply the proposed hybrid rotation averaging approach to incremental Structure from Motion (SfM), the accuracy and robustness of SfM are both improved by adding the resulting global rotations as regularisers to bundle adjustment. Overall, we demonstrate high practicality of the proposed method as bad camera poses are effectively corrected and drift is reduced. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Hybrid_Rotation_Averaging_A_Fast_and_Robust_Rotation_Averaging_Approach_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.09116 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Hybrid_Rotation_Averaging_A_Fast_and_Robust_Rotation_Averaging_Approach_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Hybrid_Rotation_Averaging_A_Fast_and_Robust_Rotation_Averaging_Approach_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Hybrid_Rotation_Averaging_CVPR_2021_supplemental.pdf | null |
One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation | Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu | Point cloud semantic segmentation often requires largescale annotated training data, but clearly, point-wise labels are too tedious to prepare. While some recent methods propose to train a 3D network with small percentages of point labels, we take the approach to an extreme and propose "One Thing One Click," meaning that the annotator only needs to label one point per object. To leverage these extremely sparse labels in network training, we design a novel self-training approach, in which we iteratively conduct the training and label propagation, facilitated by a graph propagation module. Also, we adopt a relation network to generate per-category prototype and explicitly model the similarity among graph nodes to generate pseudo labels to guide the iterative training. Experimental results on both ScanNet-v2 and S3DIS show that our self-training approach, with extremely-sparse annotations, outperforms all existing weakly supervised methods for 3D semantic segmentation by a large margin, and our results are also comparable to those of the fully supervised counterparts. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_One_Thing_One_Click_A_Self-Training_Approach_for_Weakly_Supervised_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02246 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_One_Thing_One_Click_A_Self-Training_Approach_for_Weakly_Supervised_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_One_Thing_One_Click_A_Self-Training_Approach_for_Weakly_Supervised_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_One_Thing_One_CVPR_2021_supplemental.pdf | null |
Out-of-Distribution Detection Using Union of 1-Dimensional Subspaces | Alireza Zaeemzadeh, Niccolo Bisagno, Zeno Sambugaro, Nicola Conci, Nazanin Rahnavard, Mubarak Shah | The goal of out-of-distribution (OOD) detection is to handle the situations where the test samples are drawn from a different distribution than the training data. In this paper, we argue that OOD samples can be detected more easily if the training data is embedded into a low-dimensional space, such that the embedded training samples lie on a union of 1-dimensional subspaces. We show that such embedding of the in-distribution (ID) samples provides us with two main advantages. First, due to compact representation in the feature space, OOD samples are less likely to occupy the same region as the known classes. Second, the first singular vector of ID samples belonging to a 1-dimensional subspace can be used as their robust representative. Motivated by these observations, we train a deep neural network such that the ID samples are embedded onto a union of 1-dimensional subspaces. At the test time, employing sampling techniques used for approximate Bayesian inference in deep learning, input samples are detected as OOD if they occupy the region corresponding to the ID samples with probability 0. Spectral components of the ID samples are used as robust representative of this region. Our method does not have any hyperparameter to be tuned using extra information and it can be applied on different modalities with minimal change. The effectiveness of the proposed method is demonstrated on different benchmark datasets, both in the image and video classification domains. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zaeemzadeh_Out-of-Distribution_Detection_Using_Union_of_1-Dimensional_Subspaces_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zaeemzadeh_Out-of-Distribution_Detection_Using_Union_of_1-Dimensional_Subspaces_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zaeemzadeh_Out-of-Distribution_Detection_Using_Union_of_1-Dimensional_Subspaces_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zaeemzadeh_Out-of-Distribution_Detection_Using_CVPR_2021_supplemental.pdf | null |
MP3: A Unified Model To Map, Perceive, Predict and Plan | Sergio Casas, Abbas Sadat, Raquel Urtasun | High-definition maps (HD maps) are a key component of most modern self-driving systems due to their valuable semantic and geometric information. Unfortunately, building HD maps has proven hard to scale due to their cost as well as the requirements they impose in the localization system that has to work everywhere with centimeter-level accuracy. Being able to drive without an HD map would be very beneficial to scale self-driving solutions as well as to increase the failure tolerance of existing ones (e.g., if localization fails or the map is not up-to-date). Towards this goal, we propose an end-to-end approach to mapless driving where the input is raw sensor data and a high-level command (e.g., turn left at the intersection). We then predict intermediate representations in the form of an online map and the current and future state of dynamic agents, and exploit them in a novel neural motion planner to make interpretable decisions taking into account uncertainty. We show that our approach is significantly safer, more comfortable, and can follow commands better than the baselines in challenging long-term closed-loop simulations, as well as when compared to an expert driver in a large-scale real-world dataset. | https://openaccess.thecvf.com/content/CVPR2021/papers/Casas_MP3_A_Unified_Model_To_Map_Perceive_Predict_and_Plan_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.06806 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Casas_MP3_A_Unified_Model_To_Map_Perceive_Predict_and_Plan_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Casas_MP3_A_Unified_Model_To_Map_Perceive_Predict_and_Plan_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Casas_MP3_A_Unified_CVPR_2021_supplemental.zip | null |
SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elements | Qianli Ma, Shunsuke Saito, Jinlong Yang, Siyu Tang, Michael J. Black | Learning to model and reconstruct humans in clothing is challenging due to articulation, non-rigid deformation, and varying clothing types and topologies. To enable learning, the choice of representation is the key. Recent work uses neural networks to parameterize local surface elements. This approach captures locally coherent geometry and non-planar details, can deal with varying topology, and does not require registered training data. However, naively using such methods to model 3D clothed humans fails to capture fine-grained local deformations and generalizes poorly. To address this, we present three key innovations: First, we deform surface elements based on a human body model such that large-scale deformations caused by articulation are explicitly separated from topological changes and local clothing deformations. Second, we address the limitations of existing neural surface elements by regressing local geometry from local features, significantly improving the expressiveness. Third, we learn a pose embedding on a 2D parameterization space that encodes posed body geometry, improving generalization to unseen poses by reducing non-local spurious correlations. We demonstrate the efficacy of our surface representation by learning models of complex clothing from point clouds. The clothing can change topology and deviate from the topology of the body. Once learned, we can animate previously unseen motions, producing high-quality point clouds, from which we generate realistic images with neural rendering. We assess the importance of each technical contribution and show that our approach outperforms the state-of-the-art methods in terms of reconstruction accuracy and inference time. The code is available for research purposes at https://qianlim.github.io/SCALE. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ma_SCALE_Modeling_Clothed_Humans_with_a_Surface_Codec_of_Articulated_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.07660 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_SCALE_Modeling_Clothed_Humans_with_a_Surface_Codec_of_Articulated_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_SCALE_Modeling_Clothed_Humans_with_a_Surface_Codec_of_Articulated_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ma_SCALE_Modeling_Clothed_CVPR_2021_supplemental.pdf | null |
Playable Video Generation | Willi Menapace, Stephane Lathuiliere, Sergey Tulyakov, Aliaksandr Siarohin, Elisa Ricci | This paper introduces the unsupervised learning problem of playable video generation (PVG). In PVG, we aim at allowing a user to control the generated video by selecting a discrete action at every time step as when playing a video game. The difficulty of the task lies both in learning semantically consistent actions and in generating realistic videos conditioned on the user input. We propose a novel framework for PVG that is trained in a self-supervised manner on a large dataset of unlabelled videos. We employ an encoder-decoder architecture where the predicted action labels act as bottleneck. The network is constrained to learn a rich action space using, as main driving loss, a reconstruction loss on the generated video. We demonstrate the effectiveness of the proposed approach on several datasets with wide environment variety. Further details, code and examples are available on our project page willi-menapace.github.io/playable-video-generation-website. | https://openaccess.thecvf.com/content/CVPR2021/papers/Menapace_Playable_Video_Generation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.12195 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Menapace_Playable_Video_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Menapace_Playable_Video_Generation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Menapace_Playable_Video_Generation_CVPR_2021_supplemental.pdf | null |
AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations From Self-Trained Negative Adversaries | Qianjiang Hu, Xiao Wang, Wei Hu, Guo-Jun Qi | Contrastive learning relies on constructing a collection of negative examples that are sufficiently hard to discriminate against positive queries when their representations are self-trained. Existing contrastive learning methods either maintain a queue of negative samples over mini-batches while only a small portion of them are updated in an iteration or only use the other examples from the current minibatch as negatives. They could not closely track the change of the learned representation over iterations by updating the entire queue as a whole or discard the useful information from the past mini-batches. Alternatively, we present to directly learn a set of negative adversaries playing against the self-trained representation. Two players, the representation network and negative adversaries are alternately updated to obtain the most challenging negative examples against which the representation of positive queries will be trained to discriminate. We further show that the negative adversaries are updated towards a weighted combination of positive queries by maximizing the adversarial contrastive loss, thereby allowing them to closely track the change of representations over time. Experiment results demonstrate the proposed Adversarial Contrastive (AdCo) model not only achieves superior performances (a top-1 accuracy of 73.2% over 200 epochs and 75.7% over 800 epochs with linear evaluation on ImageNet), but also can be pre-trained more efficiently with much shorter GPU time and fewer epochs. The source code is available at https://github.com/maple-research-lab/AdCo. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_AdCo_Adversarial_Contrast_for_Efficient_Learning_of_Unsupervised_Representations_From_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.08435 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_AdCo_Adversarial_Contrast_for_Efficient_Learning_of_Unsupervised_Representations_From_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_AdCo_Adversarial_Contrast_for_Efficient_Learning_of_Unsupervised_Representations_From_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hu_AdCo_Adversarial_Contrast_CVPR_2021_supplemental.pdf | null |
Permute, Quantize, and Fine-Tune: Efficient Compression of Neural Networks | Julieta Martinez, Jashan Shewakramani, Ting Wei Liu, Ioan Andrei Barsan, Wenyuan Zeng, Raquel Urtasun | Compressing large neural networks is an important step for their deployment in resource-constrained computational platforms. In this context, vector quantization is an appealing framework that expresses multiple parameters using a single code, and has recently achieved state-of-the-art network compression on a range of core vision and natural language processing tasks. Key to the success of vector quantization is deciding which parameter groups should be compressed together. Previous work has relied on heuristics that group the spatial dimension of individual convolutional filters, but a general solution remains unaddressed. This is desirable for pointwise convolutions (which dominate modern architectures), linear layers (which have no notion of spatial dimension), and convolutions (when more than one filter is compressed to the same codeword). In this paper we make the observation that the weights of two adjacent layers can be permuted while expressing the same function. We then establish a connection to rate-distortion theory and search for permutations that result in networks that are easier to compress. Finally, we rely on an annealed quantization algorithm to better compress the network and achieve higher final accuracy. We show results on image classification, object detection, and segmentation, reducing the gap with the uncompressed model by 40 to 70% w.r.t. the current state of the art. We will release code to reproduce all our experiments. | https://openaccess.thecvf.com/content/CVPR2021/papers/Martinez_Permute_Quantize_and_Fine-Tune_Efficient_Compression_of_Neural_Networks_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Martinez_Permute_Quantize_and_Fine-Tune_Efficient_Compression_of_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Martinez_Permute_Quantize_and_Fine-Tune_Efficient_Compression_of_Neural_Networks_CVPR_2021_paper.html | CVPR 2021 | null | null |
Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis | Karren Yang, Samuel Goldman, Wengong Jin, Alex X. Lu, Regina Barzilay, Tommi Jaakkola, Caroline Uhler | In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development. Building on the recent success of graph neural networks for learning molecular embeddings and flow-based models for image generation, we propose Mol2Image: a flow-based generative model for molecule to cell image synthesis. To generate cell features at different resolutions and scale to high-resolution images, we develop a novel multi-scale flow architecture based on a Haar wavelet image pyramid. To maximize the mutual information between the generated images and the molecular interventions, we devise a training strategy based on contrastive learning. To evaluate our model, we propose a new set of metrics for biological image generation that are robust, interpretable, and relevant to practitioners. We show quantitatively that our method learns a meaningful embedding of the molecular intervention, which is translated into an image representation reflecting the biological effects of the intervention. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Mol2Image_Improved_Conditional_Flow_Models_for_Molecule_to_Image_Synthesis_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Mol2Image_Improved_Conditional_Flow_Models_for_Molecule_to_Image_Synthesis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Mol2Image_Improved_Conditional_Flow_Models_for_Molecule_to_Image_Synthesis_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Mol2Image_Improved_Conditional_CVPR_2021_supplemental.pdf | null |
Improved Handling of Motion Blur in Online Object Detection | Mohamed Sayed, Gabriel Brostow | We wish to detect specific categories of objects, for online vision systems that will run in the real world. Object detection is already very challenging. It is even harder when the images are blurred, from the camera being in a car or a hand-held phone. Most existing efforts either focused on sharp images, with easy to label ground truth, or they have treated motion blur as one of many generic corruptions. Instead, we focus especially on the details of egomotion induced blur. We explore five classes of remedies, where each targets different potential causes for the performance gap between sharp and blurred images. For example, first deblurring an image changes its human interpretability, but at present, only partly improves object detection. The other four classes of remedies address multi-scale texture, out-of-distribution testing, label generation, and conditioning by blur-type. Surprisingly, we discover that custom label generation aimed at resolving spatial ambiguity, ahead of all others, markedly improves object detection. Also, in contrast to findings from classification, we see a noteworthy boost by conditioning our model on bespoke categories of motion blur. We validate and cross-breed the different remedies experimentally on blurred COCO images and real-world blur datasets, producing an easy and practical favorite model with superior detection rates. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sayed_Improved_Handling_of_Motion_Blur_in_Online_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.14448 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sayed_Improved_Handling_of_Motion_Blur_in_Online_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sayed_Improved_Handling_of_Motion_Blur_in_Online_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
Multimodal Motion Prediction With Stacked Transformers | Yicheng Liu, Jinghuai Zhang, Liangji Fang, Qinhong Jiang, Bolei Zhou | Predicting multiple plausible future trajectories of the nearby vehicles is crucial for the safety of autonomous driving. Recent motion prediction approaches attempt to achieve such multimodal motion prediction by implicitly regularizing the feature or explicitly generating multiple candidate proposals. However, it remains challenging since the latent features may concentrate on the most frequent mode of the data while the proposal-based methods depend largely on the prior knowledge to generate and select the proposals. In this work, we propose a novel transformer framework for multimodal motion prediction, termed as mmTransformer. A novel network architecture based on stacked transformers is designed to model the multimodality at feature level with a set of fixed independent proposals. A region-based training strategy is then developed to induce the multimodality of the generated proposals. Experiments on Argoverse dataset show that the proposed model achieves the state-of-the-art performance on motion prediction, substantially improving the diversity and the accuracy of the predicted trajectories. Demo video and code are available at https://decisionforce. github.io/mmTransformer. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Multimodal_Motion_Prediction_With_Stacked_Transformers_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.11624 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Multimodal_Motion_Prediction_With_Stacked_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Multimodal_Motion_Prediction_With_Stacked_Transformers_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Multimodal_Motion_Prediction_CVPR_2021_supplemental.pdf | null |
The Translucent Patch: A Physical and Universal Attack on Object Detectors | Alon Zolfi, Moshe Kravchik, Yuval Elovici, Asaf Shabtai | Physical adversarial attacks against object detectors have seen increasing success in recent years. However, these attacks require direct access to the object of interest in order to apply a physical patch. Furthermore, to hide multiple objects, an adversarial patch must be applied to each object. In this paper, we propose a contactless translucent physical patch containing a carefully constructed pattern, which is placed on the camera's lens, to fool state-of-the-art object detectors. The primary goal of our patch is to hide all instances of a selected target class. In addition, the optimization method used to construct the patch aims to ensure that the detection of other (untargeted) classes remains unharmed. Therefore, in our experiments, which are conducted on state-of-the-art object detection models used in autonomous driving, we study the effect of the patch on the detection of both the selected target class and the other classes. We show that our patch was able to prevent the detection of 42.27% of all stop sign instances while maintaining high (nearly 80%) detection of the other classes. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zolfi_The_Translucent_Patch_A_Physical_and_Universal_Attack_on_Object_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.12528 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zolfi_The_Translucent_Patch_A_Physical_and_Universal_Attack_on_Object_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zolfi_The_Translucent_Patch_A_Physical_and_Universal_Attack_on_Object_CVPR_2021_paper.html | CVPR 2021 | null | null |
Exploit Visual Dependency Relations for Semantic Segmentation | Mingyuan Liu, Dan Schonfeld, Wei Tang | Dependency relations among visual entities are ubiquity because both objects and scenes are highly structured. They provide prior knowledge about the real world that can help improve the generalization ability of deep learning approaches. Different from contextual reasoning which focuses on feature aggregation in the spatial domain, visual dependency reasoning explicitly models the dependency relations among visual entities. In this paper, we introduce a novel network architecture, termed the dependency network or DependencyNet, for semantic segmentation. It unifies dependency reasoning at three semantic levels. Intra-class reasoning decouples the representations of different object categories and updates them separately based on the internal object structures. Inter-class reasoning then performs spatial and semantic reasoning based on the dependency relations among different object categories. We will have an in-depth investigation on how to discover the dependency graph from the training annotations. Global dependency reasoning further refines the representations of each object category based on the global scene information. Extensive ablative studies with a controlled model size and the same network depth show that each individual dependency reasoning component benefits semantic segmentation and they together significantly improve the base network. Experimental results on two benchmark datasets show the DependencyNet achieves comparable performance to the recent states of the art. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Exploit_Visual_Dependency_Relations_for_Semantic_Segmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Exploit_Visual_Dependency_Relations_for_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Exploit_Visual_Dependency_Relations_for_Semantic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Exploit_Visual_Dependency_CVPR_2021_supplemental.pdf | null |
Dense Label Encoding for Boundary Discontinuity Free Rotation Detection | Xue Yang, Liping Hou, Yue Zhou, Wentao Wang, Junchi Yan | Rotation detection serves as a fundamental building block in many visual applications involving aerial image, scene text, and face etc. Differing from the dominant regression-based approaches for orientation estimation, this paper explores a relatively less-studied methodology based on classification. The hope is to inherently dismiss the boundary discontinuity issue as encountered by the regression-based detectors. We propose new techniques to push its frontier in two aspects: i) new encoding mechanism: the design of two Densely Coded Labels (DCL) for angle classification, to replace the Sparsely Coded Label (SCL) in existing classification-based detectors, leading to three times training speed increase as empirically observed across benchmarks, further with notable improvement in detection accuracy; ii) loss re-weighting: we propose Angle Distance and Aspect Ratio Sensitive Weighting (ADARSW), which improves the detection accuracy especially for square-like objects, by making DCL-based detectors sensitive to angular distance and object's aspect ratio. Extensive experiments and visual analysis on large-scale public datasets for aerial images i.e. DOTA, UCAS-AOD, HRSC2016, as well as scene text dataset ICDAR2015 and MLT, show the effectiveness of our approach. The source code will be made public available. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Dense_Label_Encoding_for_Boundary_Discontinuity_Free_Rotation_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.09670 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Dense_Label_Encoding_for_Boundary_Discontinuity_Free_Rotation_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Dense_Label_Encoding_for_Boundary_Discontinuity_Free_Rotation_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On | Igor Santesteban, Nils Thuerey, Miguel A. Otaduy, Dan Casas | We propose a new generative model for 3D garment deformations that enables us to learn, for first time, a data-driven method for virtual try-on that effectively addresses garment-body collisions. In contrast to existing methods that require an undesirable postprocessing step to fix garment-body interpenetrations at test time, our approach directly outputs 3D garment configurations that do not collide with the underlying body. Key to our success is a new canonical space for garments that removes pose-and-shape deformations already captured by a new diffused human body model, which extrapolates body surface properties such as skinning weights and blendshapes to any 3D point. We leverage this representation to train a generative model with a novel self-supervised collision term that learns to reliably solve garment-body interpenetrations. We extensively evaluate and compare our results with recently proposed data-driven methods, and show that our method is the first to successfully address garment-body contact in unseen body shapes and motions, without compromising the realism and detail. | https://openaccess.thecvf.com/content/CVPR2021/papers/Santesteban_Self-Supervised_Collision_Handling_via_Generative_3D_Garment_Models_for_Virtual_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.06462 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Santesteban_Self-Supervised_Collision_Handling_via_Generative_3D_Garment_Models_for_Virtual_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Santesteban_Self-Supervised_Collision_Handling_via_Generative_3D_Garment_Models_for_Virtual_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Santesteban_Self-Supervised_Collision_Handling_CVPR_2021_supplemental.zip | null |
DexYCB: A Benchmark for Capturing Hand Grasping of Objects | Yu-Wei Chao, Wei Yang, Yu Xiang, Pavlo Molchanov, Ankur Handa, Jonathan Tremblay, Yashraj S. Narang, Karl Van Wyk, Umar Iqbal, Stan Birchfield, Jan Kautz, Dieter Fox | We introduce DexYCB, a new dataset for capturing hand grasping of objects. We first compare DexYCB with a related one through cross-dataset evaluation. We then present a thorough benchmark of state-of-the-art approaches on three relevant tasks: 2D object and keypoint detection, 6D object pose estimation, and 3D hand pose estimation. Finally, we evaluate a new robotics-relevant task: generating safe robot grasps in human-to-robot object handover. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chao_DexYCB_A_Benchmark_for_Capturing_Hand_Grasping_of_Objects_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.04631 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chao_DexYCB_A_Benchmark_for_Capturing_Hand_Grasping_of_Objects_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chao_DexYCB_A_Benchmark_for_Capturing_Hand_Grasping_of_Objects_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chao_DexYCB_A_Benchmark_CVPR_2021_supplemental.zip | null |
Prototype Completion With Primitive Knowledge for Few-Shot Learning | Baoquan Zhang, Xutao Li, Yunming Ye, Zhichao Huang, Lisai Zhang | Few-shot learning is a challenging task, which aims to learn a classifier for novel classes with few examples. Pre-training based meta-learning methods effectively tackle the problem by pre-training a feature extractor and then fine-tuning it through the nearest centroid based meta-learning. However, results show that the fine-tuning step makes very marginal improvements. In this paper, 1) we figure out the key reason, i.e., in the pre-trained feature space, the base classes already form compact clusters while novel classes spread as groups with large variances, which implies that fine-tuning the feature extractor is less meaningful; 2) instead of fine-tuning the feature extractor, we focus on estimating more representative prototypes during meta-learning. Consequently, we propose a novel prototype completion based meta-learning framework. This framework first introduces primitive knowledge (i.e., class-level part or attribute annotations) and extracts representative attribute features as priors. Then, we design a prototype completion network to learn to complete prototypes with these priors. To avoid the prototype completion error caused by primitive knowledge noises or class differences, we further develop a Gaussian based prototype fusion strategy that combines the mean-based and completed prototypes by exploiting the unlabeled samples. Extensive experiments show that our method: (i) can obtain more accurate prototypes; (ii) outperforms state-of-the-art techniques by 2% 9% in terms of classification accuracy. Our code is available online. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Prototype_Completion_With_Primitive_Knowledge_for_Few-Shot_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2009.04960 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Prototype_Completion_With_Primitive_Knowledge_for_Few-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Prototype_Completion_With_Primitive_Knowledge_for_Few-Shot_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Prototype_Completion_With_CVPR_2021_supplemental.pdf | null |
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