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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.
CRCL: Causal Representation Consistency Learning for Anomaly Detection in Surveillance Videos
SlowFast-LLaVA-1.5: A Family of Token-Efficient Video Large Language Models for Long-Form Video Understanding
MammAlps: A multi-view video behavior monitoring dataset of wild mammals in the Swiss Alps
Collaborative Temporal Consistency Learning for Point-supervised Natural Language Video Localization
V2P-Bench: Evaluating Video-Language Understanding with Visual Prompts for Better Human-Model Interaction
4D-Bench: Benchmarking Multi-modal Large Language Models for 4D Object Understanding
Temporal Action Detection Model Compression by Progressive Block Drop
PVChat: Personalized Video Chat with One-Shot Learning
MASH-VLM: Mitigating Action-Scene Hallucination in Video-LLMs through Disentangled Spatial-Temporal Representations
DocVideoQA: Towards Comprehensive Understanding of Document-Centric Videos through Question Answering
STOP: Integrated Spatial-Temporal Dynamic Prompting for Video Understanding
Agentic Keyframe Search for Video Question Answering
Hybrid-Level Instruction Injection for Video Token Compression in Multi-modal Large Language Models
XAttention: Block Sparse Attention with Antidiagonal Scoring
FAVOR-Bench: A Comprehensive Benchmark for Fine-Grained Video Motion Understanding
Improving LLM Video Understanding with 16 Frames Per Second
SpaceVLLM: Endowing Multimodal Large Language Model with Spatio-Temporal Video Grounding Capability
Impossible Videos
ViSpeak: Visual Instruction Feedback in Streaming Videos
Logic-in-Frames: Dynamic Keyframe Search via Visual Semantic-Logical Verification for Long Video Understanding
VideoMind: A Chain-of-LoRA Agent for Long Video Reasoning
Long-VMNet: Accelerating Long-Form Video Understanding via Fixed Memory
Towards Scalable Modeling of Compressed Videos for Efficient Action Recognition
Does Your Vision-Language Model Get Lost in the Long Video Sampling Dilemma?
AdaReTaKe: Adaptive Redundancy Reduction to Perceive Longer for Video-language Understanding
LLaVA-MLB: Mitigating and Leveraging Attention Bias for Training-Free Video LLMs
Watch and Learn: Leveraging Expert Knowledge and Language for Surgical Video Understanding
V-STaR: Benchmarking Video-LLMs on Video Spatio-Temporal Reasoning
Vamba: Understanding Hour-Long Videos with Hybrid Mamba-Transformers
TIME: Temporal-sensitive Multi-dimensional Instruction Tuning and Benchmarking for Video-LLMs
LVAgent: Long Video Understanding by Multi-Round Dynamical Collaboration of MLLM Agents
KVQ: Boosting Video Quality Assessment via Saliency-guided Local Perception
Keyframe-oriented Vision Token Pruning: Enhancing Efficiency of Large Vision Language Models on Long-Form Video Processing
Measure Twice, Cut Once: Grasping Video Structures and Event Semantics with LLMs for Video Temporal Localization
Everything Can Be Described in Words: A Simple Unified Multi-Modal Framework with Semantic and Temporal Alignment
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