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 451–475 of 1149 papers

TitleStatusHype
Four Eyes Are Better Than Two: Harnessing the Collaborative Potential of Large Models via Differentiated Thinking and Complementary Ensembles—0
ViQAgent: Zero-Shot Video Question Answering via Agent with Open-Vocabulary Grounding ValidationCode0
LiveVLM: Efficient Online Video Understanding via Streaming-Oriented KV Cache and Retrieval—0
Clapper: Compact Learning and Video Representation in VLMs—0
ViaRL: Adaptive Temporal Grounding via Visual Iterated Amplification Reinforcement Learning—0
Leveraging Foundation Models for Multimodal Graph-Based Action Recognition—0
Domain Adaptation of VLM for Soccer Video Understanding—0
A Challenge to Build Neuro-Symbolic Video AgentsCode0
Breaking Down Video LLM Benchmarks: Knowledge, Spatial Perception, or True Temporal Understanding?—0
Temporal-Oriented Recipe for Transferring Large Vision-Language Model to Video UnderstandingCode0
From Shots to Stories: LLM-Assisted Video Editing with Unified Language Representations—0
SkillFormer: Unified Multi-View Video Understanding for Proficiency Estimation—0
VCRBench: Exploring Long-form Causal Reasoning Capabilities of Large Video Language ModelsCode0
Gameplay Highlights Generation—0
Seed1.5-VL Technical Report—0
StreamBridge: Turning Your Offline Video Large Language Model into a Proactive Streaming Assistant—0
RAVU: Retrieval Augmented Video Understanding with Compositional Reasoning over Graph—0
VideoLLM Benchmarks and Evaluation: A Survey—0
Empowering Agentic Video Analytics Systems with Video Language Models—0
SeriesBench: A Benchmark for Narrative-Driven Drama Series UnderstandingCode0
TimeSoccer: An End-to-End Multimodal Large Language Model for Soccer Commentary Generation—0
DyMU: Dynamic Merging and Virtual Unmerging for Efficient VLMs—0
An LMM for Efficient Video Understanding via Reinforced Compression of Video Cubes—0
OmniV-Med: Scaling Medical Vision-Language Model for Universal Visual Understanding—0
Grounding-MD: Grounded Video-language Pre-training for Open-World Moment Detection—0
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