Kalman filter algorithm is widely used in video object tracking and other areas for its prior forecast . The drawback is that when the tracking process is running , the algorithm requires a large number of operations to update the state equation and observation equation for each frame , which leads to deteriorating the system 's real-time performance . Object occlusion is widespread in video surveillance due to the influence of angle and environment , which brings a great impact on the target tracking and makes the development of video object tracking encountered meeting many constraints . The challenge in video object tracking is how to track accurately when the target is obscured . In this paper , we have proposed and presented a method for object tracking in video that exploits new tight frame of curvelets . We constructed a method of tracking that provides a sparse expansion for typical images having smooth contours . This paper proposes the Parallel WiSARD Object Tracker ( PWOT ) , a new object tracker based on the WiSARD weightless neural network that is robust against quantization errors . Object tracking in video is an important and challenging task in many applications . Difficulties can arise due to weather conditions , target trajectory and appearance , occlusions , lighting conditions and noise . Usually , the video based object tracking deal with non-stationary image stream that changes over time . Robust and Real time moving object tracking is a problematic issue in computer vision research area . MPEG-4 simple profile enables the use of 2D mesh for video object motion tracking . The deformation of the video object is expressed in terms of the motion vectors of the mesh topology . The architecture has been prototyped using 0.35 /spl mu/m CMOS technology with three layers of metal . The proposed architecture can be used in video object motion tracking and texture warping processors . The data set can be found at https://research.google.com/youtube-bb . We hope the availability of such large curated corpus will spur new advances in video object detection and tracking . To video object tracking problem , this paper proposed an on-line learning tracking method based on co-training framework . First of all , the method adopted two different local features to build on-line Boosting model , and then , would train samples making use of co-training learning framework , which avoided the cumulative error of the model and dropping frames problem effectively . Next , a heuristic based filtering algorithm is applied on the thinned structures for locating necessary points to measure adductors angle . A semi-automatic scheme based on the object tracking of a video has been proposed to minimize errors of the image based analysis . It is observed that the video-based analysis outperforms the image-based method . Since the approaches suggested so far for video moving object tracking are sensitive to the accuracy of object segmentation , we propose a new video object tracking algorithm that provides the strength of robustness to the problems of both under-segmentation and over-segmentation . The proposed tracking algorithm introduces a concept of central macroblock , which is used to establish the correspondences between objects inside neighboring frames via two levels of similarity measurement and observations . To this end , we propose a scheduler network , which determines to detect or track at a certain frame , as a generalization of Siamese trackers . Although being light-weight and simple in structure , the scheduler network is more effective than the frame skipping baselines and flow-based approaches , as validated on ImageNet VID dataset in video object detection/tracking . section : Abstract In this paper we illustrate how to perform both visual object tracking and semi - supervised video object segmentation , in real - time , with a single simple approach . Our method , dubbed SiamMask , improves the offline training procedure of popular fully - convolutional Siamese approaches for object tracking by augmenting their loss with a binary segmentation task . Broadly there are two ways of video retrieval , one is text based video retrieval and other one is content based video retrieval . Text based video retrieval also called as tag based video retrieval . In text based video retrieval videos are retrieved based on the tags assigned to them while storing . Proposed work is a content based video retrieval system based on textual ques . Text based video retrieval is an approach that enables search based on the textual information present in the video . Regions of textual information are identified within the frames of the video . Constructing a joint representation invariant across different modalities ( e.g. , video , language ) is of significant importance in many multimedia applications . While there are a number of recent successes in developing effective image-text retrieval methods by learning joint representations , the video-text retrieval task , however , has not been explored to its fullest extent . In this paper , we study how to effectively utilize available multimodal cues from videos for the cross-modal video-text retrieval task . In this article , we propose a novel deep semantic multimodal hashing network ( DSMHN ) for scalable image-text and video-text retrieval . The proposed deep hashing framework leverages 2-D convolutional neural networks ( CNN ) as the backbone network to capture the spatial information for image-text retrieval , while the 3-D CNN as the backbone network to capture the spatial and temporal information for video-text retrieval . In the DSMHN , two sets of modality-specific hash functions are jointly learned by explicitly preserving both intermodality similarities and intramodality semantic labels . Using multimodal density estimation , we propose a noise estimation building block for multimodal representation learning that is based strictly on the inherent correlation between different modalities . We demonstrate how our noise estimation can be broadly integrated and achieves comparable results to state-of-the-art performance on five different benchmark datasets for two challenging multimodal tasks : Video Question Answering and Text-To-Video Retrieval . While testing a keyframe for a given concept , we adopt TRV as the weight vector , instead of equal weighting strategy , to combine the SVM outputs of topic-models . ( 3 ) Introduction of Pseudo Relevance Feedback ( PRF ) into our concept detection system for the purpose of making re-trained models more adaptive to the test data : unlike existing PRF techniques in text and video retrieval , we propose a preliminary strategy to explore the visual features of positive training samples to improve the quality of pseudo positive samples . Experimental results demonstrate that our proposed LDASVM approach is both effective and efficient . The clusters are also iteratively refined to ensure both feature and label-level agreement among samples . We show that our approach outperforms multiple baselines both on a new , open dataset and on the popular TRECVID corpus at both the tasks of annotation and text-based retrieval of videos . This paper presents a new approach for text-based video content retrieval system . The proposed scheme consists of three main processes that are key frame extraction , text localization and keyword matching . Concept discovery methods in such a setting train hundreds of detectors at test time and apply them to every frame in the video database . Hence , they are not practical for use in a text based video retrieval setting . We show that an efficient visual representation for a new query can be constructed on-line that enables matching against the test set in real-time . We evaluate our method on the task of video retrieval and report results for the MPII Movie Description and MSR-VTT datasets . The proposed MEE model demonstrates significant improvements and outperforms previously reported methods on both text-to-video and video-to-text retrieval tasks . But there is a huge “ semantic gap ” between what people want , how they can express , and what is available out there . With lack of satisfaction from textual based video retrieval , the idea of content based video retrieval has been the attention for researchers since long time . Our system has integrated different algorithms to propose a new approach for achieving higher efficiency in the field of content based video retrieval . Video to Text Retrieval . We participated in the matching and ranking subtask in TRECVid challenge 2017 . On the one hand , the main problems and limitations that may be encountered and should be faced when deploying real-time services over mobile wireless ad hoc networks are analyzed and discussed . Bandwidth constraints and node mobility are portrayed as the major causes that prevent good quality of service and smooth video playback . On the other hand , following then the aim of improving video streaming quality , this thesis proposes practical solutions that involve diverse routing and cross-layer techniques . In MPMTP , systematic Raptor codes are adopted to mitigate video quality degradation caused by wireless channel errors as well as to alleviate a head-of-line blocking problem in multipath environments , and their encoding parameters such as code rate , symbol size , and the number of source symbols are determined on the fly by considering the wireless channel state , Raptor encoding and decoding complexity , and receiver buffer occupancy . Furthermore , MPMTP performs packet scheduling considering not only wireless network conditions but also packet payload characteristics for smooth video playback . The proposed MPMTP is fully implemented in a Linux kernel and examined over real wireless network environments . Relationships and Relationship Types . Case in Point : Determining entity classes , attributes and relationship types for video sales . 4 . Object-Oriented Data Models . Case in Point : E-R model for video sales for BigHit Video . III . Complements launched early in a platform 's life cycle face an audience entirely of early platform adopters , whereas later-launching complements face a mixed audience of both early and late adopters , and we argue that differences in preferences and behavior between early and late adopters affect whether complements will succeed and which types will be most successful . We explore these dynamics in the context of the console video game industry using a unique data set of 2,918 video games released in the United Kingdom from 2000 to 2007 . We show that despite the increase in the potential user pool as the platform evolves , video games launched later in the platform life cycle realize lower sales than those launched earlier . The final study evaluated the fitness potential of the game " Dance Central 2 " both by itself and with extra game content , which was unlocked based on the participant 's performance . Significant contributions include a framework for running a longitudinal video game study , results indicating high engagement with some fitness potential , and discussion of how dance games could make exercise a more enjoyable experience . In the wild , even the best streaming video mechanisms result in low-resolution , low-frame-rate video , in part because the motion of first-person mobile video ( e.g. , via a head-mounted camera ) decimates temporal ( inter-frame ) compression . We introduce a visualization technique for displaying low-bit-rate first-person video that maintains the benefits of high resolution , while minimizing the problems typically associated with low frame rates . This technique has the unexpected benefit of eliminating the " Blaire Witch Project " effect-the nausea-inducing jumpiness typical of first-person video . Segmentation of an object from a video is a challenging task in multimedia applications . Depending on the application , automatic or interactive methods are desired ; however , regardless of the application type , efficient computation of video object segmentation is crucial for time-critical applications ; specifically , mobile and interactive applications require near real-time efficiencies . In this paper , we address the problem of video segmentation from the perspective of efficiency . With the relentless digitization of commercial media , it is increasingly susceptible to the violation of its intellectual property rights . We address the issue of authenticating digital video in this paper by presenting a novel technique based on motion trajectory and cryptographic secret sharing . Given a video , we segment it into shots and select the key frames in a shot based on the motion trajectory . Many interesting and promising prototypes for visualizing video data have been proposed , including those that combine videos with their spatial context ( contextualized videos ) . However , relatively little work has investigated the fundamental design factors behind these prototypes in order to provide general design guidance . Moreover , a case study was exemplfied on an edge cloud assisted HTTP adaptive video streaming to demonstrate the effectiveness of cloud computing support . Finally , by envisioning a list of future research topics in cloud-based video communication and networking a coclusion is made . The system comprises a comprehensive management platform , a core layer switch , a monitoring module , a storage module and a display module ; the comprehensive management platform controls the monitoring module , the storage module and the display module via the core layer switch ; a video encoder and a splicing processor are arranged between the core layer switch and the display module ; and the splicing processor is connected with the comprehensive management platform . According to the video monitoring system , the intelligent degree is high and the operation is simple ; through transmitting a video to the display module of a monitoring center for centralized displaying , the system has functions such as multi-picture monitoring , round touring and the like , and a corresponding relation of monitoring information with a physical position thereof can be embodied ; the real-time monitoring on internal and external environments of a community is realized ; the video storage function , the video playback and inquiry , the dynamic monitoring function requirement , and the multi-system linkage function are realized , and the overall safety protection ability of the community is improved . This is because the multiple softmaxes are able to capture a larger number of distinct contexts in the text than a single output . Perhaps our Transformer also captures the features relevant to a large number of distinct contexts , and the finetuning is able to select the most significant of these features , while ignoring those features that – while adding value in general – are not appropriate in a video game setting . section : Conclusion The aggregation scheme is simple with small computation and memory footprints , but can generate quality face representations after training . The proposed NAN can be used for general video or set representation , and we plan to apply it to other vision tasks in our future work . section : [ reference ] . Our problem statement is different in that our input is a pixelwise semantic layout , and our technical approach differs substantially in that a single feedforward convolutional network is trained end - to - end to synthesize a high - resolution image . A line of work considers synthesis of future frames in video . Srivastava et al . This article studies supervised video summarization by formulating it into a sequence-to-sequence learning framework , in which the input and output are sequences of original video frames and their predicted importance scores , respectively . Two critical issues are addressed in this article : short-term contextual attention insufficiency and distribution inconsistency . In this paper , we address the visual video summarization problem in a Bayesian framework in order to detect and describe the underlying temporal transformation symmetries in a video sequence . Given a set of time correlated frames , we attempt to extract a reduced number of image-like data structures which are semantically meaningful and that have the ability of representing the sequence evolution .
Kalman filter algorithm is widely used in video object tracking and other areas for its prior forecast . The drawback is that when the tracking process is running , the algorithm requires a large number of operations to update the state equation and observation equation for each frame , which leads to deteriorating the system 's real-time performance .
Object occlusion is widespread in video surveillance due to the influence of angle and environment , which brings a great impact on the target tracking and makes the development of video object tracking encountered meeting many constraints . The challenge in video object tracking is how to track accurately when the target is obscured .
In this paper , we have proposed and presented a method for object tracking in video that exploits new tight frame of curvelets . We constructed a method of tracking that provides a sparse expansion for typical images having smooth contours .
This paper proposes the Parallel WiSARD Object Tracker ( PWOT ) , a new object tracker based on the WiSARD weightless neural network that is robust against quantization errors . Object tracking in video is an important and challenging task in many applications . Difficulties can arise due to weather conditions , target trajectory and appearance , occlusions , lighting conditions and noise .
Usually , the video based object tracking deal with non-stationary image stream that changes over time . Robust and Real time moving object tracking is a problematic issue in computer vision research area .
MPEG-4 simple profile enables the use of 2D mesh for video object motion tracking . The deformation of the video object is expressed in terms of the motion vectors of the mesh topology .
The architecture has been prototyped using 0.35 /spl mu/m CMOS technology with three layers of metal . The proposed architecture can be used in video object motion tracking and texture warping processors .
The data set can be found at https://research.google.com/youtube-bb . We hope the availability of such large curated corpus will spur new advances in video object detection and tracking .
To video object tracking problem , this paper proposed an on-line learning tracking method based on co-training framework . First of all , the method adopted two different local features to build on-line Boosting model , and then , would train samples making use of co-training learning framework , which avoided the cumulative error of the model and dropping frames problem effectively .
Next , a heuristic based filtering algorithm is applied on the thinned structures for locating necessary points to measure adductors angle . A semi-automatic scheme based on the object tracking of a video has been proposed to minimize errors of the image based analysis . It is observed that the video-based analysis outperforms the image-based method .
Since the approaches suggested so far for video moving object tracking are sensitive to the accuracy of object segmentation , we propose a new video object tracking algorithm that provides the strength of robustness to the problems of both under-segmentation and over-segmentation . The proposed tracking algorithm introduces a concept of central macroblock , which is used to establish the correspondences between objects inside neighboring frames via two levels of similarity measurement and observations .
To this end , we propose a scheduler network , which determines to detect or track at a certain frame , as a generalization of Siamese trackers . Although being light-weight and simple in structure , the scheduler network is more effective than the frame skipping baselines and flow-based approaches , as validated on ImageNet VID dataset in video object detection/tracking .
section : Abstract In this paper we illustrate how to perform both visual object tracking and semi - supervised video object segmentation , in real - time , with a single simple approach . Our method , dubbed SiamMask , improves the offline training procedure of popular fully - convolutional Siamese approaches for object tracking by augmenting their loss with a binary segmentation task .
Broadly there are two ways of video retrieval , one is text based video retrieval and other one is content based video retrieval . Text based video retrieval also called as tag based video retrieval . In text based video retrieval videos are retrieved based on the tags assigned to them while storing .
Proposed work is a content based video retrieval system based on textual ques . Text based video retrieval is an approach that enables search based on the textual information present in the video . Regions of textual information are identified within the frames of the video .
Constructing a joint representation invariant across different modalities ( e.g. , video , language ) is of significant importance in many multimedia applications . While there are a number of recent successes in developing effective image-text retrieval methods by learning joint representations , the video-text retrieval task , however , has not been explored to its fullest extent . In this paper , we study how to effectively utilize available multimodal cues from videos for the cross-modal video-text retrieval task .
In this article , we propose a novel deep semantic multimodal hashing network ( DSMHN ) for scalable image-text and video-text retrieval . The proposed deep hashing framework leverages 2-D convolutional neural networks ( CNN ) as the backbone network to capture the spatial information for image-text retrieval , while the 3-D CNN as the backbone network to capture the spatial and temporal information for video-text retrieval . In the DSMHN , two sets of modality-specific hash functions are jointly learned by explicitly preserving both intermodality similarities and intramodality semantic labels .
Using multimodal density estimation , we propose a noise estimation building block for multimodal representation learning that is based strictly on the inherent correlation between different modalities . We demonstrate how our noise estimation can be broadly integrated and achieves comparable results to state-of-the-art performance on five different benchmark datasets for two challenging multimodal tasks : Video Question Answering and Text-To-Video Retrieval .
While testing a keyframe for a given concept , we adopt TRV as the weight vector , instead of equal weighting strategy , to combine the SVM outputs of topic-models . ( 3 ) Introduction of Pseudo Relevance Feedback ( PRF ) into our concept detection system for the purpose of making re-trained models more adaptive to the test data : unlike existing PRF techniques in text and video retrieval , we propose a preliminary strategy to explore the visual features of positive training samples to improve the quality of pseudo positive samples . Experimental results demonstrate that our proposed LDASVM approach is both effective and efficient .
The clusters are also iteratively refined to ensure both feature and label-level agreement among samples . We show that our approach outperforms multiple baselines both on a new , open dataset and on the popular TRECVID corpus at both the tasks of annotation and text-based retrieval of videos .
This paper presents a new approach for text-based video content retrieval system . The proposed scheme consists of three main processes that are key frame extraction , text localization and keyword matching .
Concept discovery methods in such a setting train hundreds of detectors at test time and apply them to every frame in the video database . Hence , they are not practical for use in a text based video retrieval setting . We show that an efficient visual representation for a new query can be constructed on-line that enables matching against the test set in real-time .
We evaluate our method on the task of video retrieval and report results for the MPII Movie Description and MSR-VTT datasets . The proposed MEE model demonstrates significant improvements and outperforms previously reported methods on both text-to-video and video-to-text retrieval tasks .
But there is a huge “ semantic gap ” between what people want , how they can express , and what is available out there . With lack of satisfaction from textual based video retrieval , the idea of content based video retrieval has been the attention for researchers since long time . Our system has integrated different algorithms to propose a new approach for achieving higher efficiency in the field of content based video retrieval .
Video to Text Retrieval . We participated in the matching and ranking subtask in TRECVid challenge 2017 .
On the one hand , the main problems and limitations that may be encountered and should be faced when deploying real-time services over mobile wireless ad hoc networks are analyzed and discussed . Bandwidth constraints and node mobility are portrayed as the major causes that prevent good quality of service and smooth video playback . On the other hand , following then the aim of improving video streaming quality , this thesis proposes practical solutions that involve diverse routing and cross-layer techniques .
In MPMTP , systematic Raptor codes are adopted to mitigate video quality degradation caused by wireless channel errors as well as to alleviate a head-of-line blocking problem in multipath environments , and their encoding parameters such as code rate , symbol size , and the number of source symbols are determined on the fly by considering the wireless channel state , Raptor encoding and decoding complexity , and receiver buffer occupancy . Furthermore , MPMTP performs packet scheduling considering not only wireless network conditions but also packet payload characteristics for smooth video playback . The proposed MPMTP is fully implemented in a Linux kernel and examined over real wireless network environments .
Relationships and Relationship Types . Case in Point : Determining entity classes , attributes and relationship types for video sales . 4 .
Object-Oriented Data Models . Case in Point : E-R model for video sales for BigHit Video . III .
Complements launched early in a platform 's life cycle face an audience entirely of early platform adopters , whereas later-launching complements face a mixed audience of both early and late adopters , and we argue that differences in preferences and behavior between early and late adopters affect whether complements will succeed and which types will be most successful . We explore these dynamics in the context of the console video game industry using a unique data set of 2,918 video games released in the United Kingdom from 2000 to 2007 . We show that despite the increase in the potential user pool as the platform evolves , video games launched later in the platform life cycle realize lower sales than those launched earlier .
The final study evaluated the fitness potential of the game " Dance Central 2 " both by itself and with extra game content , which was unlocked based on the participant 's performance . Significant contributions include a framework for running a longitudinal video game study , results indicating high engagement with some fitness potential , and discussion of how dance games could make exercise a more enjoyable experience .
In the wild , even the best streaming video mechanisms result in low-resolution , low-frame-rate video , in part because the motion of first-person mobile video ( e.g. , via a head-mounted camera ) decimates temporal ( inter-frame ) compression . We introduce a visualization technique for displaying low-bit-rate first-person video that maintains the benefits of high resolution , while minimizing the problems typically associated with low frame rates . This technique has the unexpected benefit of eliminating the " Blaire Witch Project " effect-the nausea-inducing jumpiness typical of first-person video .
Segmentation of an object from a video is a challenging task in multimedia applications . Depending on the application , automatic or interactive methods are desired ; however , regardless of the application type , efficient computation of video object segmentation is crucial for time-critical applications ; specifically , mobile and interactive applications require near real-time efficiencies . In this paper , we address the problem of video segmentation from the perspective of efficiency .
With the relentless digitization of commercial media , it is increasingly susceptible to the violation of its intellectual property rights . We address the issue of authenticating digital video in this paper by presenting a novel technique based on motion trajectory and cryptographic secret sharing . Given a video , we segment it into shots and select the key frames in a shot based on the motion trajectory .
Many interesting and promising prototypes for visualizing video data have been proposed , including those that combine videos with their spatial context ( contextualized videos ) . However , relatively little work has investigated the fundamental design factors behind these prototypes in order to provide general design guidance .
Moreover , a case study was exemplfied on an edge cloud assisted HTTP adaptive video streaming to demonstrate the effectiveness of cloud computing support . Finally , by envisioning a list of future research topics in cloud-based video communication and networking a coclusion is made .
The system comprises a comprehensive management platform , a core layer switch , a monitoring module , a storage module and a display module ; the comprehensive management platform controls the monitoring module , the storage module and the display module via the core layer switch ; a video encoder and a splicing processor are arranged between the core layer switch and the display module ; and the splicing processor is connected with the comprehensive management platform . According to the video monitoring system , the intelligent degree is high and the operation is simple ; through transmitting a video to the display module of a monitoring center for centralized displaying , the system has functions such as multi-picture monitoring , round touring and the like , and a corresponding relation of monitoring information with a physical position thereof can be embodied ; the real-time monitoring on internal and external environments of a community is realized ; the video storage function , the video playback and inquiry , the dynamic monitoring function requirement , and the multi-system linkage function are realized , and the overall safety protection ability of the community is improved .
This is because the multiple softmaxes are able to capture a larger number of distinct contexts in the text than a single output . Perhaps our Transformer also captures the features relevant to a large number of distinct contexts , and the finetuning is able to select the most significant of these features , while ignoring those features that – while adding value in general – are not appropriate in a video game setting . section : Conclusion
The aggregation scheme is simple with small computation and memory footprints , but can generate quality face representations after training . The proposed NAN can be used for general video or set representation , and we plan to apply it to other vision tasks in our future work . section :
[ reference ] . Our problem statement is different in that our input is a pixelwise semantic layout , and our technical approach differs substantially in that a single feedforward convolutional network is trained end - to - end to synthesize a high - resolution image . A line of work considers synthesis of future frames in video . Srivastava et al .
This article studies supervised video summarization by formulating it into a sequence-to-sequence learning framework , in which the input and output are sequences of original video frames and their predicted importance scores , respectively . Two critical issues are addressed in this article : short-term contextual attention insufficiency and distribution inconsistency .
In this paper , we address the visual video summarization problem in a Bayesian framework in order to detect and describe the underlying temporal transformation symmetries in a video sequence . Given a set of time correlated frames , we attempt to extract a reduced number of image-like data structures which are semantically meaningful and that have the ability of representing the sequence evolution .
Vehicle Tracking System Based on SIFT Feature A video object tracking algorithm combined Kalman filter and adaptive least squares under occlusion Object Tracking of Video Sequences in Curvelet Domain Parallel WiSARD object tracker: a ram-based tracking system A particular object tracking in an environment of multiple moving objects Low power embedded memory architecture for video object decoding in MPEG4 simple profile A low power SIMD architecture for affine-based texture mapping YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video Object tracking research based on on-line learning Analysis of adductors angle measurement in Hammersmith infant neurological examinations using mean shift segmentation and feature point based object tracking Tracking Video Object Based on Central Macroblocks Detect or Track: Towards Cost-Effective Video Object Detection/Tracking Fast Online Object Tracking and Segmentation: A Unifying Approach Cricket Shots Video Retrieval using Motion Trajectory TEXTUAL QUERY BASED SPORTS VIDEO RETRIEVAL BY EMBEDDED TEXT RECOGNITION Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieval Deep Semantic Multimodal Hashing Network for Scalable Image-Text and Video-Text Retrievals. Noise Estimation Using Density Estimation for Self-Supervised Multimodal Learning TRECVID 2008 Participation by MCG-ICT-CAS CRMActive: An Active Learning Based Approach for Effective Video Annotation and Retrieval Key frame extraction for text based video retrieval using Maximally Stable Extremal Regions Fast Automatic Video Retrieval using Web Images Learning a Text-Video Embedding from Incomplete and Heterogeneous Data Content-based Video Retrieval Using image queries Cmu-ucr-bosch @ Trecvid 2017: Video to Text Retrieval Hierarchical routing and cross-layer mechanisms for improving video streaming quality of service over mobile wireless ad hoc networks MPMTP: Multipath Multimedia Transport Protocol using Systematic Raptor Codes over Wireless Networks Database Management with Web Site Development Applications Demand Heterogeneity in Platform Markets: Implications for Complementors Bridging The Gap Between Fun And Fitness: Instructional Techniques And Real-world Applications For Full-body Dance Games A Robust Abstraction for First-Person Video Streaming: Techniques, Applications, and Experiments Efficient MRF Energy Propagation for Video Segmentation via Bilateral Filters Motion trajectory based video authentication Effects of Video Placement and Spatial Context Presentation on Path Reconstruction Tasks with Contextualized Videos Cloud-based video communication and networking: an architectural overview Community intelligent video monitoring system Practical Text Classification With Large Pre-Trained Language Models Neural Aggregation Network for Video Face Recognition Photographic Image Synthesis with Cascaded Refinement Networks Deep Attentive Video Summarization With Distribution Consistency Learning. An EM algorithm for video summarization, generative model approach
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["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Medicine","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Business"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Medicine","Computer Science"] ["Computer Science"]
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CISP-BMEI CVPR AAAI 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ICMR TRECVID TRECVID ISM ISCAS (3) ArXiv 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE International Conference on Computer Vision (ICCV) ICCV
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