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 631–640 of 1149 papers

TitleStatusHype
Action Sensitivity Learning for Temporal Action Localization—0
Action Understanding with Multiple Classes of Actors—0
Actor-Action Semantic Segmentation with Grouping Process Models—0
AdaCM^2: On Understanding Extremely Long-Term Video with Adaptive Cross-Modality Memory Reduction—0
AdaCM^2: On Understanding Extremely Long-Term Video with Adaptive Cross-Modality Memory Reduction—0
AdapNet: Adaptability Decomposing Encoder-Decoder Network for Weakly Supervised Action Recognition and Localization—0
Adapting Pre-trained 3D Models for Point Cloud Video Understanding via Cross-frame Spatio-temporal Perception—0
Adaptive Intermediate Representations for Video Understanding—0
Adaptive Video Understanding Agent: Enhancing efficiency with dynamic frame sampling and feedback-driven reasoning—0
AdaTP: Attention-Debiased Token Pruning for Video Large Language Models—0
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