In order to learn the long-term dependencies in plant growth from the images , we propose to employ a Convolutional LSTM based framework . Especially , We apply an encoder-decoder model inspired by a framework on future frame prediction to model the representation of plant growth effectively . In addition , we propose two additional loss terms to put the constraints on shape changes of leaves between consecutive images . Moreover , if the predicted future is too short , it may not be fully usable by a human or other system . In this paper , we propose a novel method for future video prediction capable of generating multiple long-term futures . This makes the predictions more suitable for real applications . The disentangled representation can be used for a range of tasks . For example , applying a standard LSTM to the time-vary components enables prediction of future frames . We evaluate our approach on a range of synthetic and real videos , demonstrating the ability to coherently generate hundreds of steps into the future . For this purpose , a Deep Convolutional Neural Network ( CNN ) topology is trained with an Adversarial loss in order to produce realistic future frames of the input image sequences . The prediction of future frames is incurred by two different approaches . In the first approach , a network is receiving 4 frames and predicting the next frame and in the second approach , given a sequence of 8 input images a network is predicting the next 8 frames . We propose a hierarchical approach for making long-term predictions of future frames . To avoid inherent compounding errors in recursive pixel-level prediction , we propose to first estimate high-level structure in the input frames , then predict how that structure evolves in the future , and finally by observing a single frame from the past and the predicted high-level structure , we construct the future frames without having to observe any of the pixel-level predictions . Author(s ) : Mahmud , Tahmida Binte | Advisor(s ) : Roy-Chowdhury , Amit K | Abstract : Near-future prediction in videos has crucial impact on a wide range of practical applications which require anticipatory response . In videos , prediction can be performed in different spaces such as labels , captions and frames . The present invention relates to video prediction and video encoding and decoding accompanied with the video prediction . Disclosed is a method for predicting a video , wherein : reference information indicative of at least one reference image for prediction of images is determined ; a first reference list and a second reference list , which include the reference information and a reference sequence of reference images , are determined ; and images indicated by the first reference list and the second reference list are referred to by the same reference sequence such that restored images are generated , if the reference information indicates images only , which are for unilateral prediction on a time basis . Such random variables are sampled from simple Gaussian distributions whose means and variances are parametrized by the output of convolutional encoders over the visual history . We introduce novel convolutional architectures for predicting future body joint trajectories that outperform fully connected alternatives \cite{DBLP : journals/corr/WalkerDGH16}. We introduce stochastic spatial transformers through optical flow warping for predicting future frames , which outperform their deterministic equivalents \cite{DBLP : journals/corr/PatrauceanHC15}. Training stochastic networks involves an intractable marginalization over stochastic variables . We compare various training schemes that handle such marginalization through a ) straightforward sampling from the prior , b ) conditional variational autoencoders \cite{NIPS2015_5775,DBLP : journals/corr/WalkerDGH16 } , and , c ) a proposed K-best-sample loss that penalizes the best prediction under a fixed " prediction budget " . Predicting future frames in videos has become a promising direction of research for both computer vision and robot learning communities . The core of this problem involves moving object capture and future motion prediction . Predicting future video frames is extremely challenging , as there are many factors of variation that make up the dynamics of how frames change through time . Previously proposed solutions require complex inductive biases inside network architectures with highly specialized computation , including segmentation masks , optical flow , and foreground and background separation . Prior and concurrent works have conditioned GANs on discrete labels , text , and , indeed , images . The image - conditional models have tackled image prediction from a normal map , future frame prediction , product photo generation , and image generation from sparse annotations ( c.f . for an autoregressive approach to the same problem ) . Several other papers have also used GANs for image - to - image mappings , but only applied the GAN unconditionally , relying on other terms ( such as L2 regression ) to force the output to be conditioned on the input . The symmetry in the inputs and outputs of the language model at each step lends itself to a simple decoding operation . It can be cast as a ( pseudo ) language modeling problem in “ reverse ” , where the future prediction acts as the input and the last token acts as the target of prediction . The token embedding matrix and weights of the linear decoder of the main language model can be reused in the past decoding operation . The selected diviseme instances are time warped and blended to construct the mouth animation . Objective and subjective evaluations on the synthesized mouth animations prove that the multimodal diviseme instance selection algorithm proposed in this paper outperforms the triphone unit selection algorithm in Video Rewrite . Clear , accurate , smooth mouth animations can be obtained matching well with the pronunciation and intensity changes in the incoming speech . Multimedia service providers can benefit from IMS to ensure a good QoE ( Quality of Experience ) to their customers with minimal resources usage . In this paper , we propose an intelligent media distribution IMS system architecture for delivering video streaming . The system is based primarily on uploading a multimedia file to a server in the IMS . This paper studies the problem of mobile video delivery in heterogenous wireless networks from a server to multihomed device . Most existing works only consider delivering video streaming on single path which bandwidth is limited causing ultimate video transmission rate . To solve this live video streaming transmission bottleneck problem , we propose a novel solution named Joint Data Allocation and Fountain Coding ( JDAFC ) method that contain below characters : ( 1 ) path selection , ( 2 ) dynamic data allocation , and ( 3 ) fountain coding . HTTP Adaptive Streaming ( HAS ) is the de-facto standard for video delivery over the Internet . It enables the dynamic adaptation of video quality by splitting the video clip into small segments and providing multiple quality levels per segment . Current HAS streaming services typically utilize segments of equal durations . Video surveillance has become the main tool due to its rich , intuitive and accurate information . However , with the large-scale construction of video surveillance systems all over the world , problems such as “ useful information and clues can not be found immediately with video big data ” decrease detecting efficiency during crime prediction and public security governance . The increasing need of video based applications issues the importance of parsing and organizing the content in videos . Video surveillance has become the main tool due to its rich , intuitive and accurate information . However , with the large-scale construction of video surveillance systems all over the world , problems such as “ useful information and clues can not be found immediately with video big data ” decrease detecting efficiency during crime prediction and public security governance . This paper concludes a novel architecture for next generation public security system , and the “ front+back ” pattern is adopted . In this paper , a data mapping program is improved to solve the problems in video trandmission such as delay , throughput , packet loss in wireless Mesh network . Data of different importance is mapped to access categories in IEEE 802.11e . Lower power emission brings less interference to other devices , and larger bandwidth makes UWB less affected by interference from others , which are very attractive attributes in a household environment . However , the effective and efficient utilization of such high data rate wireless channel represents a new challenge to WPANMedia Access Control ( MAC ) , especially for high quality video streaming applications . To meet the minimum bandwidth and maximum delay requirement for Quality-of-Service ( QoS ) guarantee , high-definition Videos are a widely-used kind of resource for online learning . This paper presents an empirical study of how video production decisions affect student engagement in online educational videos . To our knowledge , ours is the largest-scale study of video engagement to date , using data from 6.9 million video watching sessions across four courses on the edX MOOC platform . Software reverse engineering is an engineering process analyzing a system for specific purposes such as identifying interrelationship between system components or reorganizing the system structure . The HELISCOPE project aims to develop an unmanned helicopter and its on-flight embedded computing system for navigation and real-time transmission of motion video using wireless communication schemes . The OFP ( Operational Flight Program ) in HELISCOPE project keeps only informal and non-standardized documents and has made us difficult to analyze and test it thoroughly . The invention discloses a hotel video playback control method comprising the following steps : ( 1 ) distributing one corresponding fixed IP address for all terminals , and grading the programme watching permission ; ( 2 ) selecting one programme by the terminal , and making a playback request for the controller ; ( 3 ) distinguishing a watching permission level according to the IP address of the terminal making the request by the controller ; and ( 4 ) according to the watching permission level of the IP address of the terminal making the request , analyzing the requested programme to a corresponding video server address . The video playback control method can be used for easily realizing the permission control , the control of pay content and free content and safety control of the video of each room ; all network devices are almost based on the DHCP/DNS ( dynamic host configuration protocol/domain name service ) protocol , and no other development costs are required ; meanwhile , the video can be controlled by the method in real time , and video service can be immediately stopped if the charge is overdue . The choice of these algorithms was based on their computational complexity and parallel structure . The aim of the experiments was to assess whether the current generation of low-power , mobile GPUs has sufficient power for running live analysis of video surveillance streams , e.g. in smart cameras , while maintaining energy consumption at a reasonable level . Tests were performed with both a synthetic benchmark and a real video surveillance recording . Differently , only the filter weights are learned , not the sampling locations like ours . This work is applied for video and stereo prediction . Combination of low level filters Gaussian filters and its smooth derivatives are widely used to extract low level image structures such as corners , edges , T - junctions , etc . For instance , the network shown in fig : slow - fusion , for which and for , the number of features in a layer network for SR would be 6 , 8 , 12 , 24 , 24 , . subsection : Spatio - temporal video SR subsubsection : Single vs multi frame early fusion Low resolution is akin to myopic vision in that fine visual features are not discernable . The drive to high image and video resolutions in multiple industries is a testament to resolution 's importance . Our model synthesizes images by progressive refinement , and going up an octave in resolution ( e.g. , from 512p to 1024p ) amounts to adding a single refinement module . NetWarp utilizes optical flow of adjacent frames to warp internal features across time space in video sequences . We note when a good - accuracy fast image semantic - segmentation framework comes into existence , video segmentation will also be benefited . section : Image Cascade Network In recent years , supervised video summarization has achieved promising progress with various recurrent neural networks ( RNNs ) based methods , which treats video summarization as a sequence-to-sequence learning problem to exploit temporal dependency among video frames across variable ranges . However , RNN has limitations in modelling the long-term temporal dependency for summarizing videos with thousands of frames due to the restricted memory storage unit . The paper introduces a novel approach to unsupervised text summarization , which in principle should work for any domain or genre . The novelty lies in exploiting the diversity of concepts in text for summarization , which has not received much attention in the summarization literature . This paper proposes a new semantic video representation for few and zero example event detection and unsupervised video event summarization . Different from existing works , which obtain a semantic representation by training concepts over images or entire video clips , we propose an algorithm that learns a set of relevant frames as the concept prototypes from web video examples , without the need for frame-level annotations , and use them for representing an event video . Especially in the context of acoustic surveillance of real-life outdoor environments , there is a high demand on effectiveness and reliability of audio signal processing techniques . This paper summarizes key technology used for unsupervised audio summarization and shows how this technology is integrated into an innovative monitoring system . Additionally , we add a diversity constraint to the model in order to decrease appearance of repeated information in a summary . We introduce DRIM ( Diversed SLIM ) algorithm that performs unsupervised summarization , both generic and query-based , and does not require parameter tuning . We evaluate our summarizer on texts in English , but it can be easily extended to other languages . In the supervised summarization setting , and given true clusterings of decision-related utterances , we find that token-level summaries that employ discourse context can approach an upper bound for decision abstracts derived directly from dialogue acts . In the unsupervised summarization setting , we find that summaries based on unsupervised partitioning of decision-related utterances perform comparably to those based on partitions generated using supervised techniques ( 0.22 ROUGE-F1 using LDA-based topic models vs. 0.23 using SVMs ) . Besides , entanglement of neurons on recurrent neural network ( RNN ) can make it troublesome to interpret and develop these networks . In order to solve these issues , in this study , a method that uses deep reinforcement learning together with independently recurrent neural networks ( IndRNN ) is proposed for unsupervised video summarization problem . In this way , the model can be trained with more steps and has more layers without having any problem related to gradients . This research strives for natural language moment retrieval in long , untrimmed video streams . The problem is not trivial especially when a video contains multiple moments of interests and the language describes complex temporal dependencies , which often happens in real scenarios . Query-based moment retrieval aims to localize the most relevant moment in an untrimmed video according to the given natural language query . Existing works often only focus on one aspect of this emerging task , such as the query representation learning , video context modeling or multi-modal fusion , thus fail to develop a comprehensive system for further performance improvement .
In order to learn the long-term dependencies in plant growth from the images , we propose to employ a Convolutional LSTM based framework . Especially , We apply an encoder-decoder model inspired by a framework on future frame prediction to model the representation of plant growth effectively . In addition , we propose two additional loss terms to put the constraints on shape changes of leaves between consecutive images .
Moreover , if the predicted future is too short , it may not be fully usable by a human or other system . In this paper , we propose a novel method for future video prediction capable of generating multiple long-term futures . This makes the predictions more suitable for real applications .
The disentangled representation can be used for a range of tasks . For example , applying a standard LSTM to the time-vary components enables prediction of future frames . We evaluate our approach on a range of synthetic and real videos , demonstrating the ability to coherently generate hundreds of steps into the future .
For this purpose , a Deep Convolutional Neural Network ( CNN ) topology is trained with an Adversarial loss in order to produce realistic future frames of the input image sequences . The prediction of future frames is incurred by two different approaches . In the first approach , a network is receiving 4 frames and predicting the next frame and in the second approach , given a sequence of 8 input images a network is predicting the next 8 frames .
We propose a hierarchical approach for making long-term predictions of future frames . To avoid inherent compounding errors in recursive pixel-level prediction , we propose to first estimate high-level structure in the input frames , then predict how that structure evolves in the future , and finally by observing a single frame from the past and the predicted high-level structure , we construct the future frames without having to observe any of the pixel-level predictions .
Author(s ) : Mahmud , Tahmida Binte | Advisor(s ) : Roy-Chowdhury , Amit K | Abstract : Near-future prediction in videos has crucial impact on a wide range of practical applications which require anticipatory response . In videos , prediction can be performed in different spaces such as labels , captions and frames .
The present invention relates to video prediction and video encoding and decoding accompanied with the video prediction . Disclosed is a method for predicting a video , wherein : reference information indicative of at least one reference image for prediction of images is determined ; a first reference list and a second reference list , which include the reference information and a reference sequence of reference images , are determined ; and images indicated by the first reference list and the second reference list are referred to by the same reference sequence such that restored images are generated , if the reference information indicates images only , which are for unilateral prediction on a time basis .
Such random variables are sampled from simple Gaussian distributions whose means and variances are parametrized by the output of convolutional encoders over the visual history . We introduce novel convolutional architectures for predicting future body joint trajectories that outperform fully connected alternatives \cite{DBLP : journals/corr/WalkerDGH16}. We introduce stochastic spatial transformers through optical flow warping for predicting future frames , which outperform their deterministic equivalents \cite{DBLP : journals/corr/PatrauceanHC15}. Training stochastic networks involves an intractable marginalization over stochastic variables . We compare various training schemes that handle such marginalization through a ) straightforward sampling from the prior , b ) conditional variational autoencoders \cite{NIPS2015_5775,DBLP : journals/corr/WalkerDGH16 } , and , c ) a proposed K-best-sample loss that penalizes the best prediction under a fixed " prediction budget " .
Predicting future frames in videos has become a promising direction of research for both computer vision and robot learning communities . The core of this problem involves moving object capture and future motion prediction .
Predicting future video frames is extremely challenging , as there are many factors of variation that make up the dynamics of how frames change through time . Previously proposed solutions require complex inductive biases inside network architectures with highly specialized computation , including segmentation masks , optical flow , and foreground and background separation .
Prior and concurrent works have conditioned GANs on discrete labels , text , and , indeed , images . The image - conditional models have tackled image prediction from a normal map , future frame prediction , product photo generation , and image generation from sparse annotations ( c.f . for an autoregressive approach to the same problem ) . Several other papers have also used GANs for image - to - image mappings , but only applied the GAN unconditionally , relying on other terms ( such as L2 regression ) to force the output to be conditioned on the input .
The symmetry in the inputs and outputs of the language model at each step lends itself to a simple decoding operation . It can be cast as a ( pseudo ) language modeling problem in “ reverse ” , where the future prediction acts as the input and the last token acts as the target of prediction . The token embedding matrix and weights of the linear decoder of the main language model can be reused in the past decoding operation .
The selected diviseme instances are time warped and blended to construct the mouth animation . Objective and subjective evaluations on the synthesized mouth animations prove that the multimodal diviseme instance selection algorithm proposed in this paper outperforms the triphone unit selection algorithm in Video Rewrite . Clear , accurate , smooth mouth animations can be obtained matching well with the pronunciation and intensity changes in the incoming speech .
Multimedia service providers can benefit from IMS to ensure a good QoE ( Quality of Experience ) to their customers with minimal resources usage . In this paper , we propose an intelligent media distribution IMS system architecture for delivering video streaming . The system is based primarily on uploading a multimedia file to a server in the IMS .
This paper studies the problem of mobile video delivery in heterogenous wireless networks from a server to multihomed device . Most existing works only consider delivering video streaming on single path which bandwidth is limited causing ultimate video transmission rate . To solve this live video streaming transmission bottleneck problem , we propose a novel solution named Joint Data Allocation and Fountain Coding ( JDAFC ) method that contain below characters : ( 1 ) path selection , ( 2 ) dynamic data allocation , and ( 3 ) fountain coding .
HTTP Adaptive Streaming ( HAS ) is the de-facto standard for video delivery over the Internet . It enables the dynamic adaptation of video quality by splitting the video clip into small segments and providing multiple quality levels per segment . Current HAS streaming services typically utilize segments of equal durations .
Video surveillance has become the main tool due to its rich , intuitive and accurate information . However , with the large-scale construction of video surveillance systems all over the world , problems such as “ useful information and clues can not be found immediately with video big data ” decrease detecting efficiency during crime prediction and public security governance . The increasing need of video based applications issues the importance of parsing and organizing the content in videos .
Video surveillance has become the main tool due to its rich , intuitive and accurate information . However , with the large-scale construction of video surveillance systems all over the world , problems such as “ useful information and clues can not be found immediately with video big data ” decrease detecting efficiency during crime prediction and public security governance . This paper concludes a novel architecture for next generation public security system , and the “ front+back ” pattern is adopted .
In this paper , a data mapping program is improved to solve the problems in video trandmission such as delay , throughput , packet loss in wireless Mesh network . Data of different importance is mapped to access categories in IEEE 802.11e .
Lower power emission brings less interference to other devices , and larger bandwidth makes UWB less affected by interference from others , which are very attractive attributes in a household environment . However , the effective and efficient utilization of such high data rate wireless channel represents a new challenge to WPANMedia Access Control ( MAC ) , especially for high quality video streaming applications . To meet the minimum bandwidth and maximum delay requirement for Quality-of-Service ( QoS ) guarantee , high-definition
Videos are a widely-used kind of resource for online learning . This paper presents an empirical study of how video production decisions affect student engagement in online educational videos . To our knowledge , ours is the largest-scale study of video engagement to date , using data from 6.9 million video watching sessions across four courses on the edX MOOC platform .
Software reverse engineering is an engineering process analyzing a system for specific purposes such as identifying interrelationship between system components or reorganizing the system structure . The HELISCOPE project aims to develop an unmanned helicopter and its on-flight embedded computing system for navigation and real-time transmission of motion video using wireless communication schemes . The OFP ( Operational Flight Program ) in HELISCOPE project keeps only informal and non-standardized documents and has made us difficult to analyze and test it thoroughly .
The invention discloses a hotel video playback control method comprising the following steps : ( 1 ) distributing one corresponding fixed IP address for all terminals , and grading the programme watching permission ; ( 2 ) selecting one programme by the terminal , and making a playback request for the controller ; ( 3 ) distinguishing a watching permission level according to the IP address of the terminal making the request by the controller ; and ( 4 ) according to the watching permission level of the IP address of the terminal making the request , analyzing the requested programme to a corresponding video server address . The video playback control method can be used for easily realizing the permission control , the control of pay content and free content and safety control of the video of each room ; all network devices are almost based on the DHCP/DNS ( dynamic host configuration protocol/domain name service ) protocol , and no other development costs are required ; meanwhile , the video can be controlled by the method in real time , and video service can be immediately stopped if the charge is overdue .
The choice of these algorithms was based on their computational complexity and parallel structure . The aim of the experiments was to assess whether the current generation of low-power , mobile GPUs has sufficient power for running live analysis of video surveillance streams , e.g. in smart cameras , while maintaining energy consumption at a reasonable level . Tests were performed with both a synthetic benchmark and a real video surveillance recording .
Differently , only the filter weights are learned , not the sampling locations like ours . This work is applied for video and stereo prediction . Combination of low level filters Gaussian filters and its smooth derivatives are widely used to extract low level image structures such as corners , edges , T - junctions , etc .
For instance , the network shown in fig : slow - fusion , for which and for , the number of features in a layer network for SR would be 6 , 8 , 12 , 24 , 24 , . subsection : Spatio - temporal video SR subsubsection : Single vs multi frame early fusion
Low resolution is akin to myopic vision in that fine visual features are not discernable . The drive to high image and video resolutions in multiple industries is a testament to resolution 's importance . Our model synthesizes images by progressive refinement , and going up an octave in resolution ( e.g. , from 512p to 1024p ) amounts to adding a single refinement module .
NetWarp utilizes optical flow of adjacent frames to warp internal features across time space in video sequences . We note when a good - accuracy fast image semantic - segmentation framework comes into existence , video segmentation will also be benefited . section : Image Cascade Network
In recent years , supervised video summarization has achieved promising progress with various recurrent neural networks ( RNNs ) based methods , which treats video summarization as a sequence-to-sequence learning problem to exploit temporal dependency among video frames across variable ranges . However , RNN has limitations in modelling the long-term temporal dependency for summarizing videos with thousands of frames due to the restricted memory storage unit .
The paper introduces a novel approach to unsupervised text summarization , which in principle should work for any domain or genre . The novelty lies in exploiting the diversity of concepts in text for summarization , which has not received much attention in the summarization literature .
This paper proposes a new semantic video representation for few and zero example event detection and unsupervised video event summarization . Different from existing works , which obtain a semantic representation by training concepts over images or entire video clips , we propose an algorithm that learns a set of relevant frames as the concept prototypes from web video examples , without the need for frame-level annotations , and use them for representing an event video .
Especially in the context of acoustic surveillance of real-life outdoor environments , there is a high demand on effectiveness and reliability of audio signal processing techniques . This paper summarizes key technology used for unsupervised audio summarization and shows how this technology is integrated into an innovative monitoring system .
Additionally , we add a diversity constraint to the model in order to decrease appearance of repeated information in a summary . We introduce DRIM ( Diversed SLIM ) algorithm that performs unsupervised summarization , both generic and query-based , and does not require parameter tuning . We evaluate our summarizer on texts in English , but it can be easily extended to other languages .
In the supervised summarization setting , and given true clusterings of decision-related utterances , we find that token-level summaries that employ discourse context can approach an upper bound for decision abstracts derived directly from dialogue acts . In the unsupervised summarization setting , we find that summaries based on unsupervised partitioning of decision-related utterances perform comparably to those based on partitions generated using supervised techniques ( 0.22 ROUGE-F1 using LDA-based topic models vs. 0.23 using SVMs ) .
Besides , entanglement of neurons on recurrent neural network ( RNN ) can make it troublesome to interpret and develop these networks . In order to solve these issues , in this study , a method that uses deep reinforcement learning together with independently recurrent neural networks ( IndRNN ) is proposed for unsupervised video summarization problem . In this way , the model can be trained with more steps and has more layers without having any problem related to gradients .
This research strives for natural language moment retrieval in long , untrimmed video streams . The problem is not trivial especially when a video contains multiple moments of interests and the language describes complex temporal dependencies , which often happens in real scenarios .
Query-based moment retrieval aims to localize the most relevant moment in an untrimmed video according to the given natural language query . Existing works often only focus on one aspect of this emerging task , such as the query representation learning , video context modeling or multi-modal fusion , thus fail to develop a comprehensive system for further performance improvement .
Plant Growth Prediction using Convolutional LSTM Long-Term Video Generation of Multiple Futures Using Human Poses Unsupervised Learning of Disentangled Representations from Video Sky Image forecasting with Generative Adversarial Networks for cloud coverage prediction Learning to Generate Long-term Future via Hierarchical Prediction Near-Future Prediction in Videos: Applications in Video Annotation and Frame Reconstruction Video prediction method capable of performing bilateral prediction and unilateral prediction and a device thereof, video encoding method and device thereof, and video decoding method and device thereof Motion Prediction Under Multimodality with Conditional Stochastic Networks Cubic LSTMs for Video Prediction High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks Image-to-Image Translation with Conditional Adversarial Networks Speech driven realistic mouth animation based on multi-modal unit selection Smart resource allocation for improving QoE in IP Multimedia Subsystems Joint Data Scheduling and FEC Coding for Multihomed Wireless Video Delivery Evaluation of the Benefits of Variable Segment Durations for Adaptive Streaming Video structured description technology based intelligence analysis of surveillance videos for public security applications The Big Data Analysis of the Next Generation Video Surveillance System for Public Security Improved Scheme for Video Transmission in Wireless Mesh Networks Based on 802.11e Distributed Reservation Algorithms for Video Streaming over WiMedia UWB Networks How video production affects student engagement: an empirical study of MOOC videos Systematic Verification of Operational Flight Program through Reverse Engineering Hotel video playback control method Performance Evaluation of Selected Parallel Object Detection and Tracking Algorithms on an Embedded GPU Platform Deformable Convolutional Networks Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation Photographic Image Synthesis with Cascaded Refinement Networks ICNet for Real-Time Semantic Segmentation on High-Resolution Images Stacked Memory Network for Video Summarization The diversity-based approach to open-domain text summarization Encoding Concept Prototypes for Video Event Detection and Summarization Unsupervised Techniques for Audio Summarization in Acoustic Environment Monitoring DRIM: MDL-Based Approach for Fast Diverse Summarization Summarizing Decisions in Spoken Meetings Unsupervised Video Summarization with Independently Recurrent Neural Networks MAN: Moment Alignment Network for Natural Language Moment Retrieval via Iterative Graph Adjustment Cross-Modal Interaction Networks for Query-Based Moment Retrieval in Videos
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VISIGRAPP (5: VISAPP) IISA AAAI NeurIPS 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) QoMEX WEB L@S FGIT-ASEA/DRBC/EL MCSS 2017 IEEE International Conference on Computer Vision (ICCV) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE International Conference on Computer Vision (ICCV) ECCV ACM Multimedia ICMR Future Security WI SIU SIGIR
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