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 76100 of 1149 papers

TitleStatusHype
QuickVideo: Real-Time Long Video Understanding with System Algorithm Co-DesignCode2
PPLLaVA: Varied Video Sequence Understanding With Prompt GuidanceCode2
QuoTA: Query-oriented Token Assignment via CoT Query Decouple for Long Video ComprehensionCode2
Scaling Video-Language Models to 10K Frames via Hierarchical Differential DistillationCode2
ST-LLM: Large Language Models Are Effective Temporal LearnersCode2
TinyLLaVA-Video: A Simple Framework of Small-scale Large Multimodal Models for Video UnderstandingCode2
OVO-Bench: How Far is Your Video-LLMs from Real-World Online Video Understanding?Code2
Attention Mechanisms in Computer Vision: A SurveyCode2
Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1Code2
E.T. Bench: Towards Open-Ended Event-Level Video-Language UnderstandingCode2
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
OmniVid: A Generative Framework for Universal Video UnderstandingCode2
Online Video Understanding: OVBench and VideoChat-OnlineCode2
Omni-Video: Democratizing Unified Video Understanding and GenerationCode2
Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video UnderstandingCode2
ActionFormer: Localizing Moments of Actions with TransformersCode2
MVBench: A Comprehensive Multi-modal Video Understanding BenchmarkCode2
A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and FutureCode2
Multi-granularity Correspondence Learning from Long-term Noisy VideosCode2
PruneVid: Visual Token Pruning for Efficient Video Large Language ModelsCode2
PyTorchVideo: A Deep Learning Library for Video UnderstandingCode2
Query-Dependent Video Representation for Moment Retrieval and Highlight DetectionCode2
AIM: Adapting Image Models for Efficient Video Action RecognitionCode2
Needle In A Video Haystack: A Scalable Synthetic Evaluator for Video MLLMsCode2
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