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 1081–1090 of 1149 papers

TitleStatusHype
SF2T: Self-supervised Fragment Finetuning of Video-LLMs for Fine-Grained Understanding—0
ShotVL: Human-Centric Highlight Frame Retrieval via Language Queries—0
SkillFormer: Unified Multi-View Video Understanding for Proficiency Estimation—0
Skimming and Scanning for Untrimmed Video Action Recognition—0
Slicing Convolutional Neural Network for Crowd Video Understanding—0
Slot-VLM: SlowFast Slots for Video-Language Modeling—0
SlowFast-LLaVA-1.5: A Family of Token-Efficient Video Large Language Models for Long-Form Video Understanding—0
SpaceVLLM: Endowing Multimodal Large Language Model with Spatio-Temporal Video Grounding Capability—0
Spacewalk-18: A Benchmark for Multimodal and Long-form Procedural Video Understanding—0
Sparse-to-Dense: A Free Lunch for Lossless Acceleration of Video Understanding in LLMs—0
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