We prove that our CWY and T-CWY methods lead to convergence to a stationary point of the training objective when coupled with stochastic gradient descent . We apply our methods to train recurrent neural network architectures in the tasks of neural machine translation and video prediction , and demonstrate superiority compared to earlier methods . After analyzing the inter-frame correlation among the images of video sequences , GHA , as neural network algorithm of PCA , is adopted to remove the transform coefficients with lower value according in order to implement video compressed sensing . Furthermore , for statistics of the adjacent frames are similar enough , the algorithm processes superiority in video frame prediction . Simulation results show that , the proposed algorithm can not only improve the reconstructed quality and the visual effects of the video sequence , but also save the sampling resources . To evaluate the proposed framework , EPM method needs to be implemented in real-world scenarios . Video popularity prediction is considered as a suitable use case for EPM . For this purpose , this thesis utilizes the ideas of EPM framework to propose a novel approach for enhancing the video popularity prediction models . Successful predictions should lead to more efficient planning while also increasing human acceptance of robot behavior . We take a novel approach by formulating this as a video prediction problem , where group splits or merges are predicted given a history of geometric social group shape transformations . We take inspiration from the success of 3D convolution models for video-related tasks . A spatial-temporal correlation technique for video data prediction is described in this paper . This technique is based on the analysis of the local correlation of video images . For auxiliary annotation , after replacing the shallow RNN part of Polygon-RNN with our 15-layer deep CBM , the performance improves by 14.7 % . For video future prediction , our deep RNN improves the state-of-the-art shallow model 's performance by 2.4 % on PSNR and SSIM . In order to alleviate the annotation bottleneck , we propose a novel self-supervised representation learning approach for spatiotemporal features extracted from videos . We introduce Skip-Clip , a method that utilizes temporal coherence in videos , by training a deep model for future clip order ranking conditioned on a context clip as a surrogate objective for video future prediction . We show that features learned using our method are generalizable and transfer strongly to downstream tasks . The present invention relates to video information prediction and video encoding/decoding using the same . The video encoding , according to the present invention , comprises the steps of : decoding a picture of a reference layer ; inducing an inter-layer reference picture by sampling the decoded picture of the reference layer ; forming a reference picture list that includes the inter-layer reference picture and a reference picture of a current layer ; generating a prediction sample for a current block by predicting the current block of the current layer on the basis of the reference picture list ; generating a residual sample for the current block on the basis of the prediction sample ; and encoding the residual sample . Weakly-supervised temporal action localization is a very challenging problem because frame-wise labels are not given in the training stage while the only hint is video-level labels : whether each video contains action frames of interest . Previous methods aggregate frame-level class scores to produce video-level prediction and learn from video-level action labels . This formulation does not fully model the problem in that background frames are forced to be misclassified as action classes to predict video-level labels accurately . In particular , by meshing a large urban area to a number of fine-grained mesh-grids , citywide crowd and traffic information in a continuous time period can be represented like a video , where each timestamp can be seen as one video frame . Based on this idea , a series of methods have been proposed to address video-like prediction for citywide crowd and traffic . In this study , we publish a new aggregated human mobility dataset generated from a real-world smartphone application and build a standard benchmark for such kind of video-like urban computing with this new dataset and the existing open datasets . To this end , a 2D-CharAnimNet is proposed . Empowered by a novel motion transfer scheme for video generation , the proposed variational-autoencoder-based model could use a relatively small dataset . In addition , to improve the fidelity of video frames , dynamic skip-connections along with a polishing generative adversarial networks are also proposed . However , because these methods rely on Gaussian assumptions , their performance is severely degraded when the prior geology is described in terms of complex facies distributions . Inspired by the impressive results obtained by deep generative networks in areas such as image and video generation , we started an investigation focused on the use of autoencoders networks to construct a continuous parameterization for facies models . In our previous publication , we combined a convolutional variational autoencoder ( VAE ) with the ensemble smoother with multiple data assimilation ( ES-MDA ) for history matching production data in models generated with multiple-point geostatistics . Our approach uses a novel L1 regularization term computed from layer features of the discriminator , which are integrated with the generator objective in the GAN model . Session constraints are also imposed on video frame generation to ensure appearance consistency between consecutive frames . We demonstrated that our model is effective at generating visually compelling facial expressions . Generating image and video is a hot topic in Deep Learning . Especially , generating video is a difficult but meaningful work . The invention discloses a method for detecting a traffic event and an electronic police device . The method comprises the steps as follows : collecting video information of traffic roads , and generating video images ; analyzing the sequentially generated video images in real time , determining a target object , and extracting the feature information of the target object ; and based on event detection and the extracted feature information of the target object , extracting the traffic event feature information of the target object , judging that the extracted traffic event feature information is the traffic event according to a preset event strategy , and outputting the traffic event feature information corresponding to the traffic event . Through application of the method , the monitoring accuracy of the traffic event can be improved and the maintenance cost can be reduced . This paper presents a new system for the generation of video rate images from voxel based volume data , in which novel approaches have been applied to the problems of shading and lighting . The shading algorithm combines a three dimensional 3 3 3 window of voxels with look up table based shading . In this paper we present a novel method for action spotting in a video sequence , that employs the 3D Fourier transform in order to define a 3D gradient correlation function operating at the frequency domain . In that way we achieve invariance to spatiotemporal variations and also to frame reordering . The modeling of actions is performed by creating sequences of actions through k-means clustering ; these sequences constitute HMM input . Third , a method of action spotting is proposed to filter meaningless actions from continuous actions and to identify precise start and end points of actions . By employing the spotter model , the proposed method improves action recognition performance . This paper provides a unified framework for the interrelated topics of action spotting , the spatiotemporal detection and localization of human actions in video , and action recognition , the classification of a given video into one of several predefined categories . A novel compact local descriptor of video dynamics in the context of action spotting and recognition is introduced based on visual spacetime oriented energy measurements . This descriptor is efficiently computed directly from raw image intensity data and thereby forgoes the problems typically associated with flow-based features . In this paper we address the action spotting problem , that is the spatiotemporal detection and localization of an action . We first calculate a novel objective function between an input video sequence and an action 's weights tensor , as acquired from a Support Tensor Machine classifier . Despite the encouraging progress these methods achieve , it is crucial to design automated approaches that only explore parts of the video which are the most relevant to the actions being searched for . To address this need , we propose the new problem of action spotting in video , which we define as finding a specific action in a video while observing a small portion of that video . Inspired by the observation that humans are extremely efficient and accurate in spotting and finding action instances in video , we propose Action Search , a novel Recurrent Neural Network approach that mimics the way humans spot actions . Moreover , to address the absence of data recording the behavior of human annotators , we put forward the Human Searches dataset , which compiles the search sequences employed by human annotators spotting actions in the AVA and THUMOS14 datasets . We consider temporal action localization as an application of the action spotting problem . Experiments on the THUMOS14 dataset reveal that our model is not only able to explore the video efficiently ( observing on average \(\mathbf {17.3}\%\ ) of the video ) but it also accurately finds human activities with \(\mathbf {30.8}\%\ ) mAP . Following previous series on Looking at People ( LAP ) challenges [ 6 , 5 , 4 ] , ChaLearn ran two competitions to be presented at CVPR 2015 : action/interaction spotting and cultural event recognition in RGB data . We ran a second round on human activity recognition on RGB data sequences . Different from many gesture-based human-robot interaction applications , which focused on the recognition of the interactional or the pointing gestures , this paper proposes a vision-based method for manipulative gesture recognition aiming to achieve natural , proactive , and non-intrusive interaction between humans and robots . The main contributions of the paper are an object-centered scheme for the segmentation and characterization of hand trajectory information , the use of particle filtering methods for an action primitive spotting , and the tight coupling of bottom-up and top-down processing that realizes a task-driven attention filter for low-level recognition steps . In contrast to purely trajectory based techniques , the presented approach is called object-oriented w.r.t . Firstly , as a proof-of-concept , we build a framework using a predefined sequence of sub-actions to model an action . We then perform experiments to show that our framework is indeed useful for action recognition and spotting . Next , we build upon our previous approach and learn sub-actions automatically rather than defining them manually . The main technical approaches to address the challenge of intellectual copyright protection are digital watermarking techniques . The spread spectrum technology has become an important technique that is not only widely used for still image and video watermarking , but also used for audio watermarking . A robust watermarking algorithm to watermark JPEG2000 compressed and encrypted images . Temporal segmentation of video is a necessary first step to indexing digital video for browsing and retrieval . A number of different video temporal segmentation algorithms have been published in the literature . In the extensive experimental validation of our proposed system , we show that the improvement in the selection power , the training of multiple students , and the increase in unlabeled data significantly improve segmentation accuracy from one generation to the next . Our method achieves top results on three challenging datasets for object discovery in video , unsupervised image segmentation , and saliency detection . At test time , our system is fast , being one to two orders of magnitude faster than published unsupervised methods . Real-time video communication as a component of distributed multimedia systems for new evolving applications like video telefony and video-conferencing is strongly dependent on the quality of service provided by its communication support . Dependability attributes of the undelying communication system playing a predominant role with regard to the quality achievable from applications and users point of view . Among these works , summaries based on a set of still frames are frequently studied and applied due to its high compactness . However , the representativeness of the selected frames , which are taken as the compact representation of the video or video segment , has not been well studied . It is observed that frame representativeness is highly related to the following elements : image quality , user attention measure , visual details , and displaying duration . In this paper , we propose buffer-based adaptation using scalable video coding for 360-degree video streaming over NDN . In the proposed method , a buffer-based adaptation algorithm and the prefetching of video tiles are used to avoid video quality switching . A simple performance evaluation using computer simulations shows that the proposed method reduces the quality switching compared with that for a rate-based adaptation algorithm . This technology can be further improved through implementing artificial intelligence , to yield faster and more accurate output . However , one of the biggest challenge of adapting video in various field is the hardware limitation . A typical video enabled IoT system usually require high performance specification , and with the addition of artificial intelligence algorithm , the requirement for the hardware performance is even higher . In this setting , a spatial light modulator ( SLM ) modulates the scene in the image domain by blocking some of the pixels at a higher resolution level , using an SLM such as a digital micromirror device . Here , we first propose a practical warm-starting method to achieve real-time reconstruction of streaming video . We then extend it by adapting a multi-hypothesis technique that takes temporal consistency into account using residual reconstruction between frames . With the second one , real-time execution is accomplished and , for this purpose , slice-level parallelism , for the main encoding loop , and block-level parallelism , for the upsampling and interpolation filtering processes , are combined . With the third one , a proper HD video content delivery under certain bit rate and end-to-end delay constraints is ensured . The experimental results prove that the proposed H.264/SVC video encoder is able to operate in real time over a wide range of target bit rates at the expense of reasonable losses in rate-distortion efficiency due to the frame partitioning into slices . Ultra-high definition video content is available in some areas such as the movie industry and scientific research . Streaming of uncompressed video is currently being done at full-high definition ( 1080p ) . In this paper we discuss the options for system which streams uncompressed ultra-high definition video content over a wireless network . In this paper , we focus on single image super - resolution ( SR ) , which aims at recovering a high - resolution ( HR ) image from a given low - resolution ( LR ) one . It is always the research emphasis because of the requirement of higher definition video displaying , such as the new generation of Ultra High Definition ( UHD ) TVs . However , most video content would not at sufficiently high resolution for UHD TVs . Therefore , the part features could be not reliable enough . This happens frequently in real applications with unconstrained video gathering environment . Simply fusing global feature and the part feature may introduces noises .
We prove that our CWY and T-CWY methods lead to convergence to a stationary point of the training objective when coupled with stochastic gradient descent . We apply our methods to train recurrent neural network architectures in the tasks of neural machine translation and video prediction , and demonstrate superiority compared to earlier methods .
After analyzing the inter-frame correlation among the images of video sequences , GHA , as neural network algorithm of PCA , is adopted to remove the transform coefficients with lower value according in order to implement video compressed sensing . Furthermore , for statistics of the adjacent frames are similar enough , the algorithm processes superiority in video frame prediction . Simulation results show that , the proposed algorithm can not only improve the reconstructed quality and the visual effects of the video sequence , but also save the sampling resources .
To evaluate the proposed framework , EPM method needs to be implemented in real-world scenarios . Video popularity prediction is considered as a suitable use case for EPM . For this purpose , this thesis utilizes the ideas of EPM framework to propose a novel approach for enhancing the video popularity prediction models .
Successful predictions should lead to more efficient planning while also increasing human acceptance of robot behavior . We take a novel approach by formulating this as a video prediction problem , where group splits or merges are predicted given a history of geometric social group shape transformations . We take inspiration from the success of 3D convolution models for video-related tasks .
A spatial-temporal correlation technique for video data prediction is described in this paper . This technique is based on the analysis of the local correlation of video images .
For auxiliary annotation , after replacing the shallow RNN part of Polygon-RNN with our 15-layer deep CBM , the performance improves by 14.7 % . For video future prediction , our deep RNN improves the state-of-the-art shallow model 's performance by 2.4 % on PSNR and SSIM .
In order to alleviate the annotation bottleneck , we propose a novel self-supervised representation learning approach for spatiotemporal features extracted from videos . We introduce Skip-Clip , a method that utilizes temporal coherence in videos , by training a deep model for future clip order ranking conditioned on a context clip as a surrogate objective for video future prediction . We show that features learned using our method are generalizable and transfer strongly to downstream tasks .
The present invention relates to video information prediction and video encoding/decoding using the same . The video encoding , according to the present invention , comprises the steps of : decoding a picture of a reference layer ; inducing an inter-layer reference picture by sampling the decoded picture of the reference layer ; forming a reference picture list that includes the inter-layer reference picture and a reference picture of a current layer ; generating a prediction sample for a current block by predicting the current block of the current layer on the basis of the reference picture list ; generating a residual sample for the current block on the basis of the prediction sample ; and encoding the residual sample .
Weakly-supervised temporal action localization is a very challenging problem because frame-wise labels are not given in the training stage while the only hint is video-level labels : whether each video contains action frames of interest . Previous methods aggregate frame-level class scores to produce video-level prediction and learn from video-level action labels . This formulation does not fully model the problem in that background frames are forced to be misclassified as action classes to predict video-level labels accurately .
In particular , by meshing a large urban area to a number of fine-grained mesh-grids , citywide crowd and traffic information in a continuous time period can be represented like a video , where each timestamp can be seen as one video frame . Based on this idea , a series of methods have been proposed to address video-like prediction for citywide crowd and traffic . In this study , we publish a new aggregated human mobility dataset generated from a real-world smartphone application and build a standard benchmark for such kind of video-like urban computing with this new dataset and the existing open datasets .
To this end , a 2D-CharAnimNet is proposed . Empowered by a novel motion transfer scheme for video generation , the proposed variational-autoencoder-based model could use a relatively small dataset . In addition , to improve the fidelity of video frames , dynamic skip-connections along with a polishing generative adversarial networks are also proposed .
However , because these methods rely on Gaussian assumptions , their performance is severely degraded when the prior geology is described in terms of complex facies distributions . Inspired by the impressive results obtained by deep generative networks in areas such as image and video generation , we started an investigation focused on the use of autoencoders networks to construct a continuous parameterization for facies models . In our previous publication , we combined a convolutional variational autoencoder ( VAE ) with the ensemble smoother with multiple data assimilation ( ES-MDA ) for history matching production data in models generated with multiple-point geostatistics .
Our approach uses a novel L1 regularization term computed from layer features of the discriminator , which are integrated with the generator objective in the GAN model . Session constraints are also imposed on video frame generation to ensure appearance consistency between consecutive frames . We demonstrated that our model is effective at generating visually compelling facial expressions .
Generating image and video is a hot topic in Deep Learning . Especially , generating video is a difficult but meaningful work .
The invention discloses a method for detecting a traffic event and an electronic police device . The method comprises the steps as follows : collecting video information of traffic roads , and generating video images ; analyzing the sequentially generated video images in real time , determining a target object , and extracting the feature information of the target object ; and based on event detection and the extracted feature information of the target object , extracting the traffic event feature information of the target object , judging that the extracted traffic event feature information is the traffic event according to a preset event strategy , and outputting the traffic event feature information corresponding to the traffic event . Through application of the method , the monitoring accuracy of the traffic event can be improved and the maintenance cost can be reduced .
This paper presents a new system for the generation of video rate images from voxel based volume data , in which novel approaches have been applied to the problems of shading and lighting . The shading algorithm combines a three dimensional 3 3 3 window of voxels with look up table based shading .
In this paper we present a novel method for action spotting in a video sequence , that employs the 3D Fourier transform in order to define a 3D gradient correlation function operating at the frequency domain . In that way we achieve invariance to spatiotemporal variations and also to frame reordering .
The modeling of actions is performed by creating sequences of actions through k-means clustering ; these sequences constitute HMM input . Third , a method of action spotting is proposed to filter meaningless actions from continuous actions and to identify precise start and end points of actions . By employing the spotter model , the proposed method improves action recognition performance .
This paper provides a unified framework for the interrelated topics of action spotting , the spatiotemporal detection and localization of human actions in video , and action recognition , the classification of a given video into one of several predefined categories . A novel compact local descriptor of video dynamics in the context of action spotting and recognition is introduced based on visual spacetime oriented energy measurements . This descriptor is efficiently computed directly from raw image intensity data and thereby forgoes the problems typically associated with flow-based features .
In this paper we address the action spotting problem , that is the spatiotemporal detection and localization of an action . We first calculate a novel objective function between an input video sequence and an action 's weights tensor , as acquired from a Support Tensor Machine classifier .
Despite the encouraging progress these methods achieve , it is crucial to design automated approaches that only explore parts of the video which are the most relevant to the actions being searched for . To address this need , we propose the new problem of action spotting in video , which we define as finding a specific action in a video while observing a small portion of that video . Inspired by the observation that humans are extremely efficient and accurate in spotting and finding action instances in video , we propose Action Search , a novel Recurrent Neural Network approach that mimics the way humans spot actions . Moreover , to address the absence of data recording the behavior of human annotators , we put forward the Human Searches dataset , which compiles the search sequences employed by human annotators spotting actions in the AVA and THUMOS14 datasets . We consider temporal action localization as an application of the action spotting problem . Experiments on the THUMOS14 dataset reveal that our model is not only able to explore the video efficiently ( observing on average \(\mathbf {17.3}\%\ ) of the video ) but it also accurately finds human activities with \(\mathbf {30.8}\%\ ) mAP .
Following previous series on Looking at People ( LAP ) challenges [ 6 , 5 , 4 ] , ChaLearn ran two competitions to be presented at CVPR 2015 : action/interaction spotting and cultural event recognition in RGB data . We ran a second round on human activity recognition on RGB data sequences .
Different from many gesture-based human-robot interaction applications , which focused on the recognition of the interactional or the pointing gestures , this paper proposes a vision-based method for manipulative gesture recognition aiming to achieve natural , proactive , and non-intrusive interaction between humans and robots . The main contributions of the paper are an object-centered scheme for the segmentation and characterization of hand trajectory information , the use of particle filtering methods for an action primitive spotting , and the tight coupling of bottom-up and top-down processing that realizes a task-driven attention filter for low-level recognition steps . In contrast to purely trajectory based techniques , the presented approach is called object-oriented w.r.t .
Firstly , as a proof-of-concept , we build a framework using a predefined sequence of sub-actions to model an action . We then perform experiments to show that our framework is indeed useful for action recognition and spotting . Next , we build upon our previous approach and learn sub-actions automatically rather than defining them manually .
The main technical approaches to address the challenge of intellectual copyright protection are digital watermarking techniques . The spread spectrum technology has become an important technique that is not only widely used for still image and video watermarking , but also used for audio watermarking . A robust watermarking algorithm to watermark JPEG2000 compressed and encrypted images .
Temporal segmentation of video is a necessary first step to indexing digital video for browsing and retrieval . A number of different video temporal segmentation algorithms have been published in the literature .
In the extensive experimental validation of our proposed system , we show that the improvement in the selection power , the training of multiple students , and the increase in unlabeled data significantly improve segmentation accuracy from one generation to the next . Our method achieves top results on three challenging datasets for object discovery in video , unsupervised image segmentation , and saliency detection . At test time , our system is fast , being one to two orders of magnitude faster than published unsupervised methods .
Real-time video communication as a component of distributed multimedia systems for new evolving applications like video telefony and video-conferencing is strongly dependent on the quality of service provided by its communication support . Dependability attributes of the undelying communication system playing a predominant role with regard to the quality achievable from applications and users point of view .
Among these works , summaries based on a set of still frames are frequently studied and applied due to its high compactness . However , the representativeness of the selected frames , which are taken as the compact representation of the video or video segment , has not been well studied . It is observed that frame representativeness is highly related to the following elements : image quality , user attention measure , visual details , and displaying duration .
In this paper , we propose buffer-based adaptation using scalable video coding for 360-degree video streaming over NDN . In the proposed method , a buffer-based adaptation algorithm and the prefetching of video tiles are used to avoid video quality switching . A simple performance evaluation using computer simulations shows that the proposed method reduces the quality switching compared with that for a rate-based adaptation algorithm .
This technology can be further improved through implementing artificial intelligence , to yield faster and more accurate output . However , one of the biggest challenge of adapting video in various field is the hardware limitation . A typical video enabled IoT system usually require high performance specification , and with the addition of artificial intelligence algorithm , the requirement for the hardware performance is even higher .
In this setting , a spatial light modulator ( SLM ) modulates the scene in the image domain by blocking some of the pixels at a higher resolution level , using an SLM such as a digital micromirror device . Here , we first propose a practical warm-starting method to achieve real-time reconstruction of streaming video . We then extend it by adapting a multi-hypothesis technique that takes temporal consistency into account using residual reconstruction between frames .
With the second one , real-time execution is accomplished and , for this purpose , slice-level parallelism , for the main encoding loop , and block-level parallelism , for the upsampling and interpolation filtering processes , are combined . With the third one , a proper HD video content delivery under certain bit rate and end-to-end delay constraints is ensured . The experimental results prove that the proposed H.264/SVC video encoder is able to operate in real time over a wide range of target bit rates at the expense of reasonable losses in rate-distortion efficiency due to the frame partitioning into slices .
Ultra-high definition video content is available in some areas such as the movie industry and scientific research . Streaming of uncompressed video is currently being done at full-high definition ( 1080p ) . In this paper we discuss the options for system which streams uncompressed ultra-high definition video content over a wireless network .
In this paper , we focus on single image super - resolution ( SR ) , which aims at recovering a high - resolution ( HR ) image from a given low - resolution ( LR ) one . It is always the research emphasis because of the requirement of higher definition video displaying , such as the new generation of Ultra High Definition ( UHD ) TVs . However , most video content would not at sufficiently high resolution for UHD TVs .
Therefore , the part features could be not reliable enough . This happens frequently in real applications with unconstrained video gathering environment . Simply fusing global feature and the part feature may introduces noises .
CWY Parametrization: a Solution for Parallelized Learning of Orthogonal and Stiefel Matrices. Compressed Sensing with Generalized Hebbian Algorithm in Video Frame Prediction Pattern profiling of users' behaviour. Group Split and Merge Prediction With 3D Convolutional Networks SPATIAL TEMPORAL PREDICTION FOR VIDEO DATA COMPRESSION Deep RNN Framework for Visual Sequential Applications Skip-Clip: Self-Supervised Spatiotemporal Representation Learning by Future Clip Order Ranking Video encoding method, video decoding method, and device using same Background Suppression Network for Weakly-supervised Temporal Action Localization VLUC: An Empirical Benchmark for Video-Like Urban Computing on Citywide Crowd and Traffic Prediction 2D character animating networks: bringing static characters to move via motion transfer Recent Developments Combining Ensemble Smoother and Deep Generative Networks for Facies History Matching A Generative Approach for Dynamically Varying Photorealistic Facial Expressions in Human-Agent Interactions Frame Difference Generative Adversarial Networks: Clearer Contour Video Generating Method for detecting traffic event and electronic police device VIDEO RATE SHADING FOR VOLUME DATA Action spotting exploiting the frequency domain Continuous Human Action Recognition Using Depth-MHI-HOG and a Spotter Model Action Spotting and Recognition Based on a Spatiotemporal Orientation Analysis Support tensor action spotting Action Search: Spotting Actions in Videos and Its Application to Temporal Action Localization ChaLearn Looking at People 2015 challenges: Action spotting and cultural event recognition An Object-Oriented Approach Using a Top-Down and Bottom-Up Process for Manipulative Action Recognition Language motivated approaches for human action recognition and spotting Comparative Study of Various Transformations in Robust Watermarking Algorithms Performance characterization and comparison of video indexing algorithms Unsupervised Learning in Space and Time over Several Generations of Teacher and Student Networks Evaluation of Video Communication over Packet Switching Networks To learn representativeness of video frames Buffer Based Adaptation Using Scalable Video Coding for 360-Degree Video Streaming over NDN HIGH DENSITY DEEP LEARNING-LITE WITH INTEL OPENVINO™ Real-time Compressive Video Reconstruction for Spatial Multiplexing Cameras A parallel H.264/SVC encoder for high definition video conferencing Uncompressed quad-1080p wireless video streaming Single image super-resolution with dilated convolution based multi-scale information learning inception module Pose-Driven Deep Convolutional Model for Person Re-identification
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AAAI SAC ICMI CANDAR Workshops CVPR Workshops ICIP ECCV (9) CVPR Workshops CVPR EDCC ACM Multimedia ICOIN GlobalSIP 2017 IEEE International Conference on Image Processing (ICIP) 2017 IEEE International Conference on Computer Vision (ICCV)
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