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Video Understanding
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A crucial task of Video Understanding is to recognise and localise (in space and time) different actions or events appearing in the video.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Semantics-aware Test-time Adaptation for 3D Human Pose Estimation
SVBench: A Benchmark with Temporal Multi-Turn Dialogues for Streaming Video Understanding
Optimizing GPT for Video Understanding: Zero-Shot Performance and Prompt Engineering
A Survey on Mamba Architecture for Vision Applications
Enhancing Video Understanding: Deep Neural Networks for Spatiotemporal Analysis
CoS: Chain-of-Shot Prompting for Long Video Understanding
A Survey on Video Analytics in Cloud-Edge-Terminal Collaborative Systems
VideoRoPE: What Makes for Good Video Rotary Position Embedding?
Long-VITA: Scaling Large Multi-modal Models to 1 Million Tokens with Leading Short-Context Accuray
Content-Rich AIGC Video Quality Assessment via Intricate Text Alignment and Motion-Aware Consistency
WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMs
A Decade of Action Quality Assessment: Largest Systematic Survey of Trends, Challenges, and Future Directions
MaxInfo: A Training-Free Key-Frame Selection Method Using Maximum Volume for Enhanced Video Understanding
LV-XAttn: Distributed Cross-Attention for Long Visual Inputs in Multimodal Large Language Models
TUMTraffic-VideoQA: A Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes
Hier-EgoPack: Hierarchical Egocentric Video Understanding with Diverse Task Perspectives
VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos
$\infty$-Video: A Training-Free Approach to Long Video Understanding via Continuous-Time Memory Consolidation
Inference-Time Text-to-Video Alignment with Diffusion Latent Beam Search
AIN: The Arabic INclusive Large Multimodal Model
Exploring the Role of Explicit Temporal Modeling in Multimodal Large Language Models for Video Understanding
Understanding Long Videos via LLM-Powered Entity Relation Graphs
TinyLLaVA-Video: A Simple Framework of Small-scale Large Multimodal Models for Video Understanding
HumanOmni: A Large Vision-Speech Language Model for Human-Centric Video Understanding
Streaming Video Understanding and Multi-round Interaction with Memory-enhanced Knowledge
Temporal Preference Optimization for Long-Form Video Understanding
VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding
A Multi-annotated and Multi-modal Dataset for Wide-angle Video Quality Assessment
InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model
MMVU: Measuring Expert-Level Multi-Discipline Video Understanding
InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling
HFGCN:Hypergraph Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition
AVS-Mamba: Exploring Temporal and Multi-modal Mamba for Audio-Visual Segmentation
Tarsier2: Advancing Large Vision-Language Models from Detailed Video Description to Comprehensive Video Understanding
Facial Dynamics in Video: Instruction Tuning for Improved Facial Expression Perception and Contextual Awareness
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