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 126–150 of 1149 papers

TitleStatusHype
VideoExpert: Augmented LLM for Temporal-Sensitive Video Understanding—0
SF2T: Self-supervised Fragment Finetuning of Video-LLMs for Fine-Grained Understanding—0
VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-TuningCode3
From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models—0
From Broadcast to Minimap: Achieving State-of-the-Art SoccerNet Game State Reconstruction—0
InstructionBench: An Instructional Video Understanding Benchmark—0
Re-thinking Temporal Search for Long-Form Video UnderstandingCode2
Learning Audio-guided Video Representation with Gated Attention for Video-Text Retrieval—0
Moment Quantization for Video Temporal Grounding—0
Scaling Video-Language Models to 10K Frames via Hierarchical Differential DistillationCode2
Aligned Better, Listen Better for Audio-Visual Large Language Models—0
Is Temporal Prompting All We Need For Limited Labeled Action Recognition?—0
TimeSearch: Hierarchical Video Search with Spotlight and Reflection for Human-like Long Video Understanding—0
SpaceR: Reinforcing MLLMs in Video Spatial ReasoningCode2
Slow-Fast Architecture for Video Multi-Modal Large Language ModelsCode1
Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1Code2
H2VU-Benchmark: A Comprehensive Benchmark for Hierarchical Holistic Video Understanding—0
DANTE-AD: Dual-Vision Attention Network for Long-Term Audio Description—0
CA^2ST: Cross-Attention in Audio, Space, and Time for Holistic Video Recognition—0
OmniMMI: A Comprehensive Multi-modal Interaction Benchmark in Streaming Video Contexts—0
BOLT: Boost Large Vision-Language Model Without Training for Long-form Video UnderstandingCode1
Mobile-VideoGPT: Fast and Accurate Video Understanding Language ModelCode2
From Trial to Triumph: Advancing Long Video Understanding via Visual Context Sample Scaling and Self-reward Alignment—0
Self-ReS: Self-Reflection in Large Vision-Language Models for Long Video Understanding—0
Bootstrap Your Own Views: Masked Ego-Exo Modeling for Fine-grained View-invariant Video RepresentationsCode0
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