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Video Recognition

Video Recognition is a process of obtaining, processing, and analysing data that it receives from a visual source, specifically video.

Papers

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Showing 126–150 of 307 papers

TitleStatusHype
Group Contextualization for Video RecognitionCode1
Gate-Shift-Fuse for Video Action RecognitionCode0
Audio-Visual Fusion Layers for Event Type Aware Video Recognition—0
Should I take a walk? Estimating Energy Expenditure from Video DataCode0
Fast Differentiable Matrix Square Root and Inverse Square RootCode1
MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video RecognitionCode1
Action Keypoint Network for Efficient Video Recognition—0
OCSampler: Compressing Videos to One Clip with Single-step SamplingCode1
Condensing a Sequence to One Informative Frame for Video Recognition—0
Optimization Planning for 3D ConvNetsCode0
Glance and Focus Networks for Dynamic Visual RecognitionCode1
Recurring the Transformer for Video Action Recognition—0
Improving Video Model Transfer With Dynamic Representation Learning—0
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video RecognitionCode1
Cross-Modal Transferable Adversarial Attacks from Images to Videos—0
DualFormer: Local-Global Stratified Transformer for Efficient Video RecognitionCode1
Auto-X3D: Ultra-Efficient Video Understanding via Finer-Grained Neural Architecture Search—0
MViTv2: Improved Multiscale Vision Transformers for Classification and DetectionCode1
TokenLearner: Adaptive Space-Time Tokenization for VideosCode1
Pooling by Sliced-Wasserstein EmbeddingCode1
Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-LearningCode1
Efficient Video Transformers with Spatial-Temporal Token SelectionCode1
Attacking Video Recognition Models with Bullet-Screen CommentsCode1
ST-ABN: Visual Explanation Taking into Account Spatio-temporal Information for Video RecognitionCode0
Temporal-attentive Covariance Pooling Networks for Video RecognitionCode1
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