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 541–550 of 1149 papers

TitleStatusHype
Learning Object State Changes in Videos: An Open-World Perspective—0
Learning Higher-order Object Interactions for Keypoint-based Video Understanding—0
Learning from Multiple Sources for Video Summarisation—0
DynTok: Dynamic Compression of Visual Tokens for Efficient and Effective Video Understanding—0
BioVL-QR: Egocentric Biochemical Vision-and-Language Dataset Using Micro QR Codes—0
An Attempt towards Interpretable Audio-Visual Video Captioning—0
AdaCM^2: On Understanding Extremely Long-Term Video with Adaptive Cross-Modality Memory Reduction—0
Learning Fine-grained View-Invariant Representations from Unpaired Ego-Exo Videos via Temporal Alignment—0
Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking—0
DynFocus: Dynamic Cooperative Network Empowers LLMs with Video Understanding—0
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