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 101150 of 1149 papers

TitleStatusHype
AIM: Adaptive Inference of Multi-Modal LLMs via Token Merging and PruningCode2
E.T. Bench: Towards Open-Ended Event-Level Video-Language UnderstandingCode2
TinyLLaVA-Video-R1: Towards Smaller LMMs for Video ReasoningCode2
SpaceR: Reinforcing MLLMs in Video Spatial ReasoningCode2
ST-LLM: Large Language Models Are Effective Temporal LearnersCode2
A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and FutureCode2
StreamingBench: Assessing the Gap for MLLMs to Achieve Streaming Video UnderstandingCode2
Is Space-Time Attention All You Need for Video Understanding?Code2
InstructSeg: Unifying Instructed Visual Segmentation with Multi-modal Large Language ModelsCode2
Streaming Video Understanding and Multi-round Interaction with Memory-enhanced KnowledgeCode2
QuickVideo: Real-Time Long Video Understanding with System Algorithm Co-DesignCode2
A Content-Driven Micro-Video Recommendation Dataset at ScaleCode2
QuoTA: Query-oriented Token Assignment via CoT Query Decouple for Long Video ComprehensionCode2
PyTorchVideo: A Deep Learning Library for Video UnderstandingCode2
AdaReTaKe: Adaptive Redundancy Reduction to Perceive Longer for Video-language UnderstandingCode2
Query-Dependent Video Representation for Moment Retrieval and Highlight DetectionCode2
video-SALMONN 2: Captioning-Enhanced Audio-Visual Large Language ModelsCode2
Beyond MOT: Semantic Multi-Object TrackingCode2
Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data EfficiencyCode2
PPLLaVA: Varied Video Sequence Understanding With Prompt GuidanceCode2
PG-Video-LLaVA: Pixel Grounding Large Video-Language ModelsCode2
PruneVid: Visual Token Pruning for Efficient Video Large Language ModelsCode2
One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object TrajectoryCode2
Adaptive Keyframe Sampling for Long Video UnderstandingCode2
Omni-Video: Democratizing Unified Video Understanding and GenerationCode2
FrameFusion: Combining Similarity and Importance for Video Token Reduction on Large Visual Language ModelsCode2
OVO-Bench: How Far is Your Video-LLMs from Real-World Online Video Understanding?Code2
Re-thinking Temporal Search for Long-Form Video UnderstandingCode2
Needle In A Video Haystack: A Scalable Synthetic Evaluator for Video MLLMsCode2
Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language ModelsCode2
Neptune: The Long Orbit to Benchmarking Long Video UnderstandingCode2
MovieChat: From Dense Token to Sparse Memory for Long Video UnderstandingCode2
Multi-granularity Correspondence Learning from Long-term Noisy VideosCode2
MMVU: Measuring Expert-Level Multi-Discipline Video UnderstandingCode2
Mobile-VideoGPT: Fast and Accurate Video Understanding Language ModelCode2
MVBench: A Comprehensive Multi-modal Video Understanding BenchmarkCode2
OmAgent: A Multi-modal Agent Framework for Complex Video Understanding with Task Divide-and-ConquerCode2
LVBench: An Extreme Long Video Understanding BenchmarkCode2
LongVLM: Efficient Long Video Understanding via Large Language ModelsCode2
LongVALE: Vision-Audio-Language-Event Benchmark Towards Time-Aware Omni-Modal Perception of Long VideosCode2
Dense Connector for MLLMsCode2
DeMamba: AI-Generated Video Detection on Million-Scale GenVideo BenchmarkCode2
LongVideoBench: A Benchmark for Long-context Interleaved Video-Language UnderstandingCode2
OmniVid: A Generative Framework for Universal Video UnderstandingCode2
Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video UnderstandingCode2
Online Video Understanding: OVBench and VideoChat-OnlineCode2
Scaling Video-Language Models to 10K Frames via Hierarchical Differential DistillationCode2
TRACE: Temporal Grounding Video LLM via Causal Event ModelingCode2
Boosting Single Image Super-Resolution via Partial Channel ShiftingCode1
Free Lunch for Surgical Video Understanding by Distilling Self-SupervisionsCode1
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