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

Showing 126150 of 1149 papers

TitleStatusHype
Free Video-LLM: Prompt-guided Visual Perception for Efficient Training-free Video LLMsCode2
PruneVid: Visual Token Pruning for Efficient Video Large Language ModelsCode2
Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data EfficiencyCode2
MVBench: A Comprehensive Multi-modal Video Understanding BenchmarkCode2
Multi-granularity Correspondence Learning from Long-term Noisy VideosCode2
Needle In A Video Haystack: A Scalable Synthetic Evaluator for Video MLLMsCode2
MMVU: Measuring Expert-Level Multi-Discipline Video UnderstandingCode2
MovieChat: From Dense Token to Sparse Memory for Long Video UnderstandingCode2
Neptune: The Long Orbit to Benchmarking Long Video UnderstandingCode2
LongVLM: Efficient Long Video Understanding via Large Language ModelsCode2
LVBench: An Extreme Long Video Understanding BenchmarkCode2
LongVALE: Vision-Audio-Language-Event Benchmark Towards Time-Aware Omni-Modal Perception of Long VideosCode2
Mobile-VideoGPT: Fast and Accurate Video Understanding Language ModelCode2
LongVideoBench: A Benchmark for Long-context Interleaved Video-Language UnderstandingCode2
Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language ModelsCode2
Dense Connector for MLLMsCode2
DeMamba: AI-Generated Video Detection on Million-Scale GenVideo BenchmarkCode2
Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video UnderstandingCode2
InstructSeg: Unifying Instructed Visual Segmentation with Multi-modal Large Language ModelsCode2
Omni-Video: Democratizing Unified Video Understanding and GenerationCode2
One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object TrajectoryCode2
OmAgent: A Multi-modal Agent Framework for Complex Video Understanding with Task Divide-and-ConquerCode2
Re-thinking Temporal Search for Long-Form Video UnderstandingCode2
Boosting Single Image Super-Resolution via Partial Channel ShiftingCode1
Leveraging triplet loss for unsupervised action segmentationCode1
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