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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 951–960 of 1149 papers

TitleStatusHype
M^33D: Learning 3D priors using Multi-Modal Masked Autoencoders for 2D image and video understanding—0
M^3Net: Multi-view Encoding, Matching, and Fusion for Few-shot Fine-grained Action Recognition—0
MaCP: Minimal yet Mighty Adaptation via Hierarchical Cosine Projection—0
Making Every Frame Matter: Continuous Video Understanding for Large Models via Adaptive State Modeling—0
MAMBA4D: Efficient Long-Sequence Point Cloud Video Understanding with Disentangled Spatial-Temporal State Space Models—0
MambaMia: A State-Space-Model-Based Compression for Efficient Video Understanding in Large Multimodal Models—0
MASH-VLM: Mitigating Action-Scene Hallucination in Video-LLMs through Disentangled Spatial-Temporal Representations—0
Massively Parallel Video Networks—0
Mavors: Multi-granularity Video Representation for Multimodal Large Language Model—0
MaxInfo: A Training-Free Key-Frame Selection Method Using Maximum Volume for Enhanced Video Understanding—0
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