This is achieved by augmenting the previous Action Micro-Tube ( AMTnet ) action detection framework in three distinct ways : by adding a parallel motion stIn this paper , we propose a new deep neural network architecture for online action detection , termed ream to the original appearance one in AMTnet ; ( 2 ) in opposition to state-of-the-art action detectors which train appearance and motion streams separately , and use a test time late fusion scheme to fuse RGB and flow cues , by jointly training both streams in an end-to-end fashion and merging RGB and optical flow features at training time ; ( 3 ) by introducing an online action tube generation algorithm which works at video-level , and in real-time ( when exploiting only appearance features ) . Two-Stream AMTnet exhibits superior action detection performance over state-of-the-art approaches on the standard action detection benchmarks . Many problems in video understanding require labeling multiple activities occurring concurrently in different parts of a video , including the objects and actors participating in such activities . However , state-of-the-art methods in computer vision focus primarily on tasks such as action classification , action detection , or action segmentation , where typically only one action label needs to be predicted . In this work , we propose a generic approach to classifying one or more nodes of a spatio-temporal graph grounded on spatially localized semantic entities in a video , such as actors and objects . ( Gaitanis , Correa and Macq , 2006 ) . The action detection problem is phrased as an inference on the underlying Dynamic Bayesian Networks representing the process of executing the actor ’s plan . DBNs are employed to track the paths to goals ( using hierarchical plan fragments ) and modifications ( strategy corrections , adaptations ) of the initial goals . This is accomplished by dividing the video into overlapping segments , then representing each segment with a 2D video projection . The advantage of the 2D projection is that it makes it convenient to apply the best techniques from object detection to the action detection problem . Our experiments show that video projection outperforms the latest results on action detection in a direct comparison . Deformable part models have achieved impressive performance for object detection , even on difficult image datasets . This paper explores the generalization of deformable part models from 2D images to 3D spatiotemporal volumes to better study their effectiveness for action detection in video . Actions are treated as spatiotemporal patterns and a deformable part model is generated for each action from a collection of examples . Abstract : Deformable part models have achieved impressive performance for object detection , even on difficult image datasets . This paper explores the generalization of deformable part models from 2D images to 3D spatiotemporal volumes to better study their effectiveness for action detection in video . Actions are treated as spatiotemporal patterns and a deformable part model is generated for each action from a collection of examples . The invention discloses an action detecting and identifying method based on radio signals and relates to the field of radio network and pervasive computing , in particular to an action detecting and identifying method based on radio signals . Radio signals are interfered by means of human actions , a universal radio device acquires radio signal data , and data is subjected to noise removal and characteristic extraction correlated with action speed to identify action . In addition , to detect a principal agent performing an action among the people appearing in a video , the spatial region proposal process was used . Further , coarse-level features contain comprehensive information of the whole video and have been frequently used in conventional action-detection studies . However , they can not provide detailed information of each person performing an action in a video . Sparse , informative feature representation has become an extremely successful method in action feature detection . Such features make the task more manageable while providing increased robustness to noise and pose variation . The standard way to obtain this information is via a manual annotation , which is costly and time consuming . Also , whereas for the third-person tasks such as object detection , the annotator can be anybody , action-object detection task requires the camera wearer to annotate the data because a third-person may not know what the camera wearer was thinking . Such a constraint makes it even more difficult to obtain first-person annotations . Several approaches have been proposed for selecting negatives , such as randomly sampling items and assuming the probability of getting a false negative is low [ 5 ] , sampling according to positive-negative similarity measures [ 8 ] , selecting the items positive for the sibling and/or ancestral categories of the category of interest as negative examples [ 6 ] . Nevertheless , hierarchical methods can not be applied in context where the categories are not structured as a hierarchy ( for instance the churn prediction for service providers [ 7 ] and the action video detection [ 8 ] ) . Furthermore , even when parent-child relationships are available for the classes being predicted , strategies based on sibling/ancestral categories may often break down , as some items are annotated to more than one sibling category , and many items have few siblings to use [ 4 ] . In this way , the STDL-Net acts as an end-to-end model which captures discriminative sample-specific spatial features at each time step and models underlying multi-scale dynamics in the MTS data . Experiments on 12 MTS benchmark datasets with explicit spatial dependencies and 3 skeleton-based action recognition tasks are conducted . The proposed STDL-Net yields better performance than existing methods and the visualized analysis demonstrates the effectiveness of learning STDUs . However , the robustness of these models to adversarial attacks remains largely unexplored due to their complex spatio-temporal nature that must represent sparse and discrete skeleton joints . This work presents the first adversarial attack on skeleton-based action recognition with graph convolutional networks . The proposed targeted attack , termed Constrained Iterative Attack for Skeleton Actions ( CIASA ) , perturbs joint locations in an action sequence such that the resulting adversarial sequence preserves the temporal coherence , spatial integrity , and the anthropomorphic plausibility of the skeletons . With the representation effectiveness , skeleton-based human action recognition has received considerable research attention , and has a wide range of real applications . In this area , many existing methods typically rely on fixed physicalconnectivity skeleton structure for recognition , which is incapable of well capturing the intrinsic high-order correlations among skeleton joints . This dissertation has been divided into five parts . In the first part , we introduce various 3D geometry-based representations for the problem of skeleton-based human action recognition . The proposed representations , referred to as R3DG features , capture the relative 3D geometry between various body parts using 3D rigid body transformations . Instead of using 3D joint coordinates as node features , we show that using relative coordinates and temporal displacements boosts performance . Our model achieves state-of-the-art performance on two challenging benchmark datasets NTURGB+D and HDM05 , for skeletal action recognition . Because of the helpfulness of action recognition of the skeleton and the development of related pose estimation techniques , action recognition based on skeleton data has drawn considerable attention and has been widely studied in recent years . In this paper , we propose an attention-based multiview re-observation fusion model for skeletal action recognition . The proposed model focuses on the factor of observation view of actions , which greatly influences action recognition . Skeleton-based action human recognition has drawn increasing attentions due to its properties of robustness and conciseness , while studies in recently years mostly have focused on extracting global motion features of skeleton but ignored the correlation among joints of local parts of skeleton . In this paper , we proposed a multi-stream network model based on local part joints motion features , our model focus on features extraction of local part joint motion and effect of fusion method on action recognition , utilizing LSTM and CNN structure a new network unit to grasp spatio-temporal information of joints in skeleton sequences . Human actions can be realized by observing the trajectories of skeleton joints . In this paper , we propose an unsupervised episodic memory learning model for skeleton based action learning and recognition . The proposed model , Multi-channel Episodic Memory Adaptive Resonance Theory ( McEM-ART ) , consists of three layers : short term memory , working memory and Episodic memory . In contrast , when deeply layered , BN-LSTM structure shows slow convergence and worse accuracy . In this work , we analyze deep-layered BN-LSTM shows slower convergence in early training phase in training scheme and , finally we propose a deep BN-LSTM structure with auxiliary classifier that is able to converge faster and gives better results at skeleton based action recognition problem . Some experiment are conducted with Penn Action dataset and our own Computer Assembling Video dataset , we verified our proposal shows better results in skeleton based action recognition . We evaluate the proposed method on three challenging skeleton datasets . Experimental results show the superiority of the proposed method for skeleton-based early action recognition . Currently most works on action recognition focus on the coarsely-grained actions , while the fine-grained action recognition is seldom addressed which is of vital importance in many applications such as video retrieval . To tackle this issue , in this paper , we release a challenging dataset by annotating the fine-grained actions in basketball game videos . Fine grained video action analysis often requires reliable detection and tracking of various interacting objects and human body parts , denoted as Interactional Object Parsing . However , most of the previous methods based on either independent or joint object detection might suffer from high model complexity and challenging image content , e.g. , illumination/pose/appearance/scale variation , motion , and occlusion etc . After passing through each LSTM node , more object detections are consolidated and thus more contextual information could be utilized to localize more difficult objects . The object parsing results are further utilized to form object specific action representation for fine grained action detection . Extensive experiments on two benchmark fine grained activity datasets demonstrate that our proposed algorithm achieves better interacting object detection performance , which in turn boosts the action recognition performance over the state-of-the-art . To this end we propose a complete end-to-end supervised learning approach that can better learn relationships between actions over time , thus improving the overall segmentation performance . The proposed hierarchical recurrent attention framework analyses the input video at multiple temporal scales , to form embeddings at frame level and segment level , and perform fine-grained action segmentation . This generates a simple , lightweight , yet extremely effective architecture for segmenting continuous video streams and has multiple application domains . Instead of pooling features uniformly from the entire video , we aggregate features in sub-regions that are more likely to contain actions according to actionness maps , which endow ADD with the capability of capturing the subtle differences between fine-grained actions . We conduct experiments on HIT Dances dataset , one of the few existing datasets for fine-grained action analysis . Quantitative results have demonstrated that ADD remarkably outperforms traditional two-stream representation . Extensive experiments are performed on the large-scale and difficult fine-grained MPII cooking action dataset . The results show that by effectively accumulating localized semantic information into the action representation and classification pipeline , we significantly improve the fine-grained action classification performance over the existing methods . The goal of the YouMakeup VQA Challenge 2020 is to provide a common benchmark for fine-grained action understanding in domain-specific videos e.g. makeup instructional videos . We propose two novel question-answering tasks to evaluate models ' fine-grained action understanding abilities . In this study , our long-term goal is to bridge the gap between computer vision and robotics by developing visual methods that can be used in real robots . In particular , this work tackles two fundamental visual problems for autonomous robotic manipulation : affordance detection and fine-grained action understanding . Theoretically , we propose different deep architectures to further improves the state of the art in each problem . Compared with general human actions , fine-grained action information is more difficult to detect and occupies relatively small-scale image regions . Our work seeks to improve fine-grained action discrimination , while also retaining the ability to perform general action recognition . Our method first estimates human pose and human parts positions in video sequences by extending our recent work on human pose tracking , and crops different scaled patches to obtain richer action information in a variety of different scales of appearance and motion cues . Fine-grained action segmentation and recognition is an important yet challenging task . Given a long , untrimmed sequence of kinematic data , the task is to classify the action at each time frame and segment the time series into the correct sequence of actions . Typical approaches decouple this problem by first extracting local spatiotemporal features from video frames and then feeding them into a temporal classifier that captures high-level temporal patterns . We describe a class of temporal models , which we call Temporal Convolutional Networks ( TCNs ) , that use a hierarchy of temporal convolutions to perform fine-grained action segmentation or detection . Our Encoder-Decoder TCN uses pooling and upsampling to efficiently capture long-range temporal patterns whereas our Dilated TCN uses dilated convolutions . We propose a systematic frame work for the automatic detection of multiple human actions within the same frame in realistic and diverse video settings . One of the major challenges is the process of recognizing and understanding of human actions from videos with large variations resulting from camera motions , changes in human appearance , pose changes , scale changes and back ground clutter etc . In this work , we present the efficient detection of multiple actions occurring simultaneously in streaming video of various real-world applications using a frame differencing-based method for background detection . We compare our method with other modeling methods ( such as multi-channel nonlinear SVM ) for multiple action detection on various video datasets . Towards this goal , we augmented the existing video dataset , Moments in Time ( MiT ) , to include over two million action labels for over one million three second videos . This multi-label dataset introduces novel challenges on how to train and analyze models for multi-action detection . Here , we present baseline results for multi-action recognition using loss functions adapted for long tail multi-label learning and provide improved methods for visualizing and interpreting models trained for multi-label action detection .
This is achieved by augmenting the previous Action Micro-Tube ( AMTnet ) action detection framework in three distinct ways : by adding a parallel motion stIn this paper , we propose a new deep neural network architecture for online action detection , termed ream to the original appearance one in AMTnet ; ( 2 ) in opposition to state-of-the-art action detectors which train appearance and motion streams separately , and use a test time late fusion scheme to fuse RGB and flow cues , by jointly training both streams in an end-to-end fashion and merging RGB and optical flow features at training time ; ( 3 ) by introducing an online action tube generation algorithm which works at video-level , and in real-time ( when exploiting only appearance features ) . Two-Stream AMTnet exhibits superior action detection performance over state-of-the-art approaches on the standard action detection benchmarks .
Many problems in video understanding require labeling multiple activities occurring concurrently in different parts of a video , including the objects and actors participating in such activities . However , state-of-the-art methods in computer vision focus primarily on tasks such as action classification , action detection , or action segmentation , where typically only one action label needs to be predicted . In this work , we propose a generic approach to classifying one or more nodes of a spatio-temporal graph grounded on spatially localized semantic entities in a video , such as actors and objects .
( Gaitanis , Correa and Macq , 2006 ) . The action detection problem is phrased as an inference on the underlying Dynamic Bayesian Networks representing the process of executing the actor ’s plan . DBNs are employed to track the paths to goals ( using hierarchical plan fragments ) and modifications ( strategy corrections , adaptations ) of the initial goals .
This is accomplished by dividing the video into overlapping segments , then representing each segment with a 2D video projection . The advantage of the 2D projection is that it makes it convenient to apply the best techniques from object detection to the action detection problem . Our experiments show that video projection outperforms the latest results on action detection in a direct comparison .
Deformable part models have achieved impressive performance for object detection , even on difficult image datasets . This paper explores the generalization of deformable part models from 2D images to 3D spatiotemporal volumes to better study their effectiveness for action detection in video . Actions are treated as spatiotemporal patterns and a deformable part model is generated for each action from a collection of examples .
Abstract : Deformable part models have achieved impressive performance for object detection , even on difficult image datasets . This paper explores the generalization of deformable part models from 2D images to 3D spatiotemporal volumes to better study their effectiveness for action detection in video . Actions are treated as spatiotemporal patterns and a deformable part model is generated for each action from a collection of examples .
The invention discloses an action detecting and identifying method based on radio signals and relates to the field of radio network and pervasive computing , in particular to an action detecting and identifying method based on radio signals . Radio signals are interfered by means of human actions , a universal radio device acquires radio signal data , and data is subjected to noise removal and characteristic extraction correlated with action speed to identify action .
In addition , to detect a principal agent performing an action among the people appearing in a video , the spatial region proposal process was used . Further , coarse-level features contain comprehensive information of the whole video and have been frequently used in conventional action-detection studies . However , they can not provide detailed information of each person performing an action in a video .
Sparse , informative feature representation has become an extremely successful method in action feature detection . Such features make the task more manageable while providing increased robustness to noise and pose variation .
The standard way to obtain this information is via a manual annotation , which is costly and time consuming . Also , whereas for the third-person tasks such as object detection , the annotator can be anybody , action-object detection task requires the camera wearer to annotate the data because a third-person may not know what the camera wearer was thinking . Such a constraint makes it even more difficult to obtain first-person annotations .
Several approaches have been proposed for selecting negatives , such as randomly sampling items and assuming the probability of getting a false negative is low [ 5 ] , sampling according to positive-negative similarity measures [ 8 ] , selecting the items positive for the sibling and/or ancestral categories of the category of interest as negative examples [ 6 ] . Nevertheless , hierarchical methods can not be applied in context where the categories are not structured as a hierarchy ( for instance the churn prediction for service providers [ 7 ] and the action video detection [ 8 ] ) . Furthermore , even when parent-child relationships are available for the classes being predicted , strategies based on sibling/ancestral categories may often break down , as some items are annotated to more than one sibling category , and many items have few siblings to use [ 4 ] .
In this way , the STDL-Net acts as an end-to-end model which captures discriminative sample-specific spatial features at each time step and models underlying multi-scale dynamics in the MTS data . Experiments on 12 MTS benchmark datasets with explicit spatial dependencies and 3 skeleton-based action recognition tasks are conducted . The proposed STDL-Net yields better performance than existing methods and the visualized analysis demonstrates the effectiveness of learning STDUs .
However , the robustness of these models to adversarial attacks remains largely unexplored due to their complex spatio-temporal nature that must represent sparse and discrete skeleton joints . This work presents the first adversarial attack on skeleton-based action recognition with graph convolutional networks . The proposed targeted attack , termed Constrained Iterative Attack for Skeleton Actions ( CIASA ) , perturbs joint locations in an action sequence such that the resulting adversarial sequence preserves the temporal coherence , spatial integrity , and the anthropomorphic plausibility of the skeletons .
With the representation effectiveness , skeleton-based human action recognition has received considerable research attention , and has a wide range of real applications . In this area , many existing methods typically rely on fixed physicalconnectivity skeleton structure for recognition , which is incapable of well capturing the intrinsic high-order correlations among skeleton joints .
This dissertation has been divided into five parts . In the first part , we introduce various 3D geometry-based representations for the problem of skeleton-based human action recognition . The proposed representations , referred to as R3DG features , capture the relative 3D geometry between various body parts using 3D rigid body transformations .
Instead of using 3D joint coordinates as node features , we show that using relative coordinates and temporal displacements boosts performance . Our model achieves state-of-the-art performance on two challenging benchmark datasets NTURGB+D and HDM05 , for skeletal action recognition .
Because of the helpfulness of action recognition of the skeleton and the development of related pose estimation techniques , action recognition based on skeleton data has drawn considerable attention and has been widely studied in recent years . In this paper , we propose an attention-based multiview re-observation fusion model for skeletal action recognition . The proposed model focuses on the factor of observation view of actions , which greatly influences action recognition .
Skeleton-based action human recognition has drawn increasing attentions due to its properties of robustness and conciseness , while studies in recently years mostly have focused on extracting global motion features of skeleton but ignored the correlation among joints of local parts of skeleton . In this paper , we proposed a multi-stream network model based on local part joints motion features , our model focus on features extraction of local part joint motion and effect of fusion method on action recognition , utilizing LSTM and CNN structure a new network unit to grasp spatio-temporal information of joints in skeleton sequences .
Human actions can be realized by observing the trajectories of skeleton joints . In this paper , we propose an unsupervised episodic memory learning model for skeleton based action learning and recognition . The proposed model , Multi-channel Episodic Memory Adaptive Resonance Theory ( McEM-ART ) , consists of three layers : short term memory , working memory and Episodic memory .
In contrast , when deeply layered , BN-LSTM structure shows slow convergence and worse accuracy . In this work , we analyze deep-layered BN-LSTM shows slower convergence in early training phase in training scheme and , finally we propose a deep BN-LSTM structure with auxiliary classifier that is able to converge faster and gives better results at skeleton based action recognition problem . Some experiment are conducted with Penn Action dataset and our own Computer Assembling Video dataset , we verified our proposal shows better results in skeleton based action recognition .
We evaluate the proposed method on three challenging skeleton datasets . Experimental results show the superiority of the proposed method for skeleton-based early action recognition .
Currently most works on action recognition focus on the coarsely-grained actions , while the fine-grained action recognition is seldom addressed which is of vital importance in many applications such as video retrieval . To tackle this issue , in this paper , we release a challenging dataset by annotating the fine-grained actions in basketball game videos .
Fine grained video action analysis often requires reliable detection and tracking of various interacting objects and human body parts , denoted as Interactional Object Parsing . However , most of the previous methods based on either independent or joint object detection might suffer from high model complexity and challenging image content , e.g. , illumination/pose/appearance/scale variation , motion , and occlusion etc .
After passing through each LSTM node , more object detections are consolidated and thus more contextual information could be utilized to localize more difficult objects . The object parsing results are further utilized to form object specific action representation for fine grained action detection . Extensive experiments on two benchmark fine grained activity datasets demonstrate that our proposed algorithm achieves better interacting object detection performance , which in turn boosts the action recognition performance over the state-of-the-art .
To this end we propose a complete end-to-end supervised learning approach that can better learn relationships between actions over time , thus improving the overall segmentation performance . The proposed hierarchical recurrent attention framework analyses the input video at multiple temporal scales , to form embeddings at frame level and segment level , and perform fine-grained action segmentation . This generates a simple , lightweight , yet extremely effective architecture for segmenting continuous video streams and has multiple application domains .
Instead of pooling features uniformly from the entire video , we aggregate features in sub-regions that are more likely to contain actions according to actionness maps , which endow ADD with the capability of capturing the subtle differences between fine-grained actions . We conduct experiments on HIT Dances dataset , one of the few existing datasets for fine-grained action analysis . Quantitative results have demonstrated that ADD remarkably outperforms traditional two-stream representation .
Extensive experiments are performed on the large-scale and difficult fine-grained MPII cooking action dataset . The results show that by effectively accumulating localized semantic information into the action representation and classification pipeline , we significantly improve the fine-grained action classification performance over the existing methods .
The goal of the YouMakeup VQA Challenge 2020 is to provide a common benchmark for fine-grained action understanding in domain-specific videos e.g. makeup instructional videos . We propose two novel question-answering tasks to evaluate models ' fine-grained action understanding abilities .
In this study , our long-term goal is to bridge the gap between computer vision and robotics by developing visual methods that can be used in real robots . In particular , this work tackles two fundamental visual problems for autonomous robotic manipulation : affordance detection and fine-grained action understanding . Theoretically , we propose different deep architectures to further improves the state of the art in each problem .
Compared with general human actions , fine-grained action information is more difficult to detect and occupies relatively small-scale image regions . Our work seeks to improve fine-grained action discrimination , while also retaining the ability to perform general action recognition . Our method first estimates human pose and human parts positions in video sequences by extending our recent work on human pose tracking , and crops different scaled patches to obtain richer action information in a variety of different scales of appearance and motion cues .
Fine-grained action segmentation and recognition is an important yet challenging task . Given a long , untrimmed sequence of kinematic data , the task is to classify the action at each time frame and segment the time series into the correct sequence of actions .
Typical approaches decouple this problem by first extracting local spatiotemporal features from video frames and then feeding them into a temporal classifier that captures high-level temporal patterns . We describe a class of temporal models , which we call Temporal Convolutional Networks ( TCNs ) , that use a hierarchy of temporal convolutions to perform fine-grained action segmentation or detection . Our Encoder-Decoder TCN uses pooling and upsampling to efficiently capture long-range temporal patterns whereas our Dilated TCN uses dilated convolutions .
We propose a systematic frame work for the automatic detection of multiple human actions within the same frame in realistic and diverse video settings . One of the major challenges is the process of recognizing and understanding of human actions from videos with large variations resulting from camera motions , changes in human appearance , pose changes , scale changes and back ground clutter etc .
In this work , we present the efficient detection of multiple actions occurring simultaneously in streaming video of various real-world applications using a frame differencing-based method for background detection . We compare our method with other modeling methods ( such as multi-channel nonlinear SVM ) for multiple action detection on various video datasets .
Towards this goal , we augmented the existing video dataset , Moments in Time ( MiT ) , to include over two million action labels for over one million three second videos . This multi-label dataset introduces novel challenges on how to train and analyze models for multi-action detection . Here , we present baseline results for multi-action recognition using loss functions adapted for long tail multi-label learning and provide improved methods for visualizing and interpreting models trained for multi-label action detection .
Two-Stream AMTnet for Action Detection Neural Message Passing on Hybrid Spatio-Temporal Visual and Symbolic Graphs for Video Understanding Plan recognition using multimodal integration Localizing actions through sequential 2D video projections Spatiotemporal Deformable Part Models for Action Detection Spatiotemporal Deformable Part Models for Action Detection (Open Access) Action detecting and identifying method based on radio signals Spatio-Temporal Action Detection in Untrimmed Videos by Using Multimodal Features and Region Proposals An Improved Method of Action Recognition Based on Sparse Spatio-temporal Features Exploiting Visual-Spatial First-Person Co-Occurrence for Action-Object Detection without Labels Selection of negatives in Hopfield networks Attention-based spatio-temporal dependence learning network Adversarial Attack on Skeleton-based Human Action Recognition Spatio-Temporal Graph Routing for Skeleton-Based Action Recognition Geometric Representations and Deep Gaussian Conditional Random field Networks for Computer Vision Part-based Graph Convolutional Network for Action Recognition Attention-Based Multiview Re-Observation Fusion Network for Skeletal Action Recognition Learning Local Part Motion Representation for Skeleton-based Action Recognition A Multi-channel Episodic Memory Model for Human Action Learning and Recognition Deep Batch-Normalized LSTM networks with Auxiliary classifier for Skeleton based Action Recognition Global Regularizer and Temporal-Aware Cross-Entropy for Skeleton-Based Early Action Recognition Fine-Grained Action Recognition on a Novel Basketball Dataset Progressively Parsing Interactional Objects for Fine Grained Action Detection Hierarchical Attention Network for Action Segmentation Add: Actionness-Pooled Deep-Convolutional Descriptor Pipelining Localized Semantic Features for Fine-Grained Action Recognition YouMakeup VQA Challenge: Towards Fine-grained Action Understanding in Domain-Specific Videos Scene Understanding for Autonomous Manipulation with Deep Learning Region-sequence based six-stream CNN features for general and fine-grained human action recognition in videos End-to-End Fine-Grained Action Segmentation and Recognition Using Conditional Random Field Models and Discriminative Sparse Coding Temporal Convolutional Networks for Action Segmentation and Detection Detection of multiple, co-occurring actions in video frames Multiple Action Detection in Videos Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding
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CVPR Workshops CVPR AIMSA AAAI ICETC SMC IPAS ACCV (4) ICASSP CVPR ICME ECCV (4) CVPR
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