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 51100 of 1149 papers

TitleStatusHype
LongVU: Spatiotemporal Adaptive Compression for Long Video-Language UnderstandingCode3
VideoGPT+: Integrating Image and Video Encoders for Enhanced Video UnderstandingCode3
SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language ModelsCode3
LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently via a Hybrid ArchitectureCode3
Video-RAG: Visually-aligned Retrieval-Augmented Long Video ComprehensionCode3
Valley2: Exploring Multimodal Models with Scalable Vision-Language DesignCode3
Hawk: Learning to Understand Open-World Video AnomaliesCode3
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language ModelsCode3
Towards Universal Soccer Video UnderstandingCode3
MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video UnderstandingCode3
VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-TuningCode3
Video ReCap: Recursive Captioning of Hour-Long VideosCode3
TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video UnderstandingCode2
Temporal Action Segmentation: An Analysis of Modern TechniquesCode2
TimeMarker: A Versatile Video-LLM for Long and Short Video Understanding with Superior Temporal Localization AbilityCode2
Streaming Video Understanding and Multi-round Interaction with Memory-enhanced KnowledgeCode2
StreamingBench: Assessing the Gap for MLLMs to Achieve Streaming Video UnderstandingCode2
SVBench: A Benchmark with Temporal Multi-Turn Dialogues for Streaming Video UnderstandingCode2
Foundation Models for Video Understanding: A SurveyCode2
TimeSuite: Improving MLLMs for Long Video Understanding via Grounded TuningCode2
AIN: The Arabic INclusive Large Multimodal ModelCode2
SpaceR: Reinforcing MLLMs in Video Spatial ReasoningCode2
AIM: Adaptive Inference of Multi-Modal LLMs via Token Merging and PruningCode2
Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data EfficiencyCode2
Re-thinking Temporal Search for Long-Form Video UnderstandingCode2
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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