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

Showing 11011149 of 1149 papers

TitleStatusHype
Joint Event Detection and Description in Continuous Video StreamsCode0
Detect-and-Track: Efficient Pose Estimation in VideosCode0
Grounded Objects and Interactions for Video Captioning0
Attend and Interact: Higher-Order Object Interactions for Video Understanding0
End-to-End Video Classification with Knowledge Graphs0
Scene-centric Joint Parsing of Cross-view Videos0
ElasticPlay: Interactive Video Summarization with Dynamic Time Budgets0
Kill Two Birds With One Stone: Boosting Both Object Detection Accuracy and Speed With adaptive Patch-of-Interest Composition0
Extensible Hierarchical Method of Detecting Interactive Actions for Video Understanding0
Unsupervised Video Understanding by Reconciliation of Posture Similarities0
Multi-kernel learning of deep convolutional features for action recognition0
Temporal Modeling Approaches for Large-scale Youtube-8M Video UnderstandingCode0
Cultivating DNN Diversity for Large Scale Video Labelling0
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 Classification0
Spatio-Temporal Vector of Locally Max Pooled Features for Action Recognition in Videos0
Jointly Learning Energy Expenditures and Activities Using Egocentric Multimodal Signals0
Generating the Future With Adversarial Transformers0
The YouTube-8M Kaggle Competition: Challenges and MethodsCode0
YouTube-8M Video Understanding Challenge Approach and Applications0
An Effective Way to Improve YouTube-8M Classification Accuracy in Google Cloud Platform0
Learnable pooling with Context Gating for video classificationCode0
The Monkeytyping Solution to the YouTube-8M Video Understanding ChallengeCode0
Learning without Prejudice: Avoiding Bias in Webly-Supervised Action Recognition0
Deep Learning Methods for Efficient Large Scale Video LabelingCode0
Large-Scale YouTube-8M Video Understanding with Deep Neural Networks0
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual ActionsCode1
Action Understanding with Multiple Classes of Actors0
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 Esports0
Generating Videos with Scene Dynamics0
VideoMCC: a New Benchmark for Video Comprehension0
Harnessing Object and Scene Semantics for Large-Scale Video Understanding0
Slicing Convolutional Neural Network for Crowd Video Understanding0
MSR-VTT: A Large Video Description Dataset for Bridging Video and Language0
The THUMOS Challenge on Action Recognition for Videos "in the Wild"0
The Open World of Micro-Videos0
Actor-Action Semantic Segmentation with Grouping Process Models0
Mid-level Representation for Visual Recognition0
Fine-Grain Annotation of Cricket Videos0
Person Count Localization in Videos From Noisy Foreground and Detections0
Unsupervised Object Discovery and Tracking in Video Collections0
Learning from Multiple Sources for Video Summarisation0
Pooled Motion Features for First-Person VideosCode0
Weakly Supervised Multiclass Video Segmentation0
Grounding Action Descriptions in Videos0
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