SOTAVerified

Video Understanding

A crucial task of Video Understanding is to recognise and localise (in space and time) different actions or events appearing in the video.

Source: Action Detection from a Robot-Car Perspective

Papers

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Showing 1101–1149 of 1149 papers

TitleStatusHype
End-to-End Learning of Motion Representation for Video UnderstandingCode0
Joint Event Detection and Description in Continuous Video StreamsCode0
Detect-and-Track: Efficient Pose Estimation in VideosCode0
Attend and Interact: Higher-Order Object Interactions for Video Understanding—0
Grounded Objects and Interactions for Video Captioning—0
End-to-End Video Classification with Knowledge Graphs—0
Scene-centric Joint Parsing of Cross-view Videos—0
ElasticPlay: Interactive Video Summarization with Dynamic Time Budgets—0
Kill Two Birds With One Stone: Boosting Both Object Detection Accuracy and Speed With adaptive Patch-of-Interest Composition—0
Extensible Hierarchical Method of Detecting Interactive Actions for Video Understanding—0
Unsupervised Video Understanding by Reconciliation of Posture Similarities—0
Multi-kernel learning of deep convolutional features for action recognition—0
Temporal Modeling Approaches for Large-scale Youtube-8M Video UnderstandingCode0
Cultivating DNN Diversity for Large Scale Video Labelling—0
Hierarchical Deep Recurrent Architecture for Video UnderstandingCode0
Video Representation Learning and Latent Concept Mining for Large-scale Multi-label Video ClassificationCode0
Aggregating Frame-level Features for Large-Scale Video Classification—0
Jointly Learning Energy Expenditures and Activities Using Egocentric Multimodal Signals—0
Spatio-Temporal Vector of Locally Max Pooled Features for Action Recognition in Videos—0
Generating the Future With Adversarial Transformers—0
The YouTube-8M Kaggle Competition: Challenges and MethodsCode0
An Effective Way to Improve YouTube-8M Classification Accuracy in Google Cloud Platform—0
YouTube-8M Video Understanding Challenge Approach and Applications—0
Learnable pooling with Context Gating for video classificationCode0
The Monkeytyping Solution to the YouTube-8M Video Understanding ChallengeCode0
Large-Scale YouTube-8M Video Understanding with Deep Neural Networks—0
Deep Learning Methods for Efficient Large Scale Video LabelingCode0
Learning without Prejudice: Avoiding Bias in Webly-Supervised Action Recognition—0
Action Understanding with Multiple Classes of Actors—0
Video Object Segmentation using Supervoxel-Based GerrymanderingCode0
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity RecognitionCode0
Temporal Tessellation: A Unified Approach for Video AnalysisCode0
Real-Time Video Highlights for Yahoo Esports—0
Generating Videos with Scene Dynamics—0
VideoMCC: a New Benchmark for Video Comprehension—0
Slicing Convolutional Neural Network for Crowd Video Understanding—0
MSR-VTT: A Large Video Description Dataset for Bridging Video and Language—0
Harnessing Object and Scene Semantics for Large-Scale Video Understanding—0
The THUMOS Challenge on Action Recognition for Videos "in the Wild"—0
The Open World of Micro-Videos—0
Actor-Action Semantic Segmentation with Grouping Process Models—0
Mid-level Representation for Visual Recognition—0
Fine-Grain Annotation of Cricket Videos—0
Person Count Localization in Videos From Noisy Foreground and Detections—0
Unsupervised Object Discovery and Tracking in Video Collections—0
Learning from Multiple Sources for Video Summarisation—0
Pooled Motion Features for First-Person VideosCode0
Weakly Supervised Multiclass Video Segmentation—0
Grounding Action Descriptions in Videos—0
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