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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.
Cross-Modal Transferable Image-to-Video Attack on Video Quality Metrics
Video Quality Assessment for Online Processing: From Spatial to Temporal Sampling
MECD+: Unlocking Event-Level Causal Graph Discovery for Video Reasoning
Aligning First, Then Fusing: A Novel Weakly Supervised Multimodal Violence Detection Method
Diversified Augmentation with Domain Adaptation for Debiased Video Temporal Grounding
VidChain: Chain-of-Tasks with Metric-based Direct Preference Optimization for Dense Video Captioning
X-LeBench: A Benchmark for Extremely Long Egocentric Video Understanding
From My View to Yours: Ego-Augmented Learning in Large Vision Language Models for Understanding Exocentric Daily Living Activities
Zero-shot Shark Tracking and Biometrics from Aerial Imagery
Valley2: Exploring Multimodal Models with Scalable Vision-Language Design
LongViTU: Instruction Tuning for Long-Form Video Understanding
LLaVA-Octopus: Unlocking Instruction-Driven Adaptive Projector Fusion for Video Understanding
OVO-Bench: How Far is Your Video-LLMs from Real-World Online Video Understanding?
H-MBA: Hierarchical MamBa Adaptation for Multi-Modal Video Understanding in Autonomous Driving
Building a Mind Palace: Structuring Environment-Grounded Semantic Graphs for Effective Long Video Analysis with LLMs
Multilevel Semantic-Aware Model for AI-Generated Video Quality Assessment
MotionBench: Benchmarking and Improving Fine-grained Video Motion Understanding for Vision Language Models
HLV-1K: A Large-scale Hour-Long Video Benchmark for Time-Specific Long Video Understanding
Unifying Specialized Visual Encoders for Video Language Models
Embodied VideoAgent: Persistent Memory from Egocentric Videos and Embodied Sensors Enables Dynamic Scene Understanding
OV-HHIR: Open Vocabulary Human Interaction Recognition Using Cross-modal Integration of Large Language Models
CaReBench: A Fine-Grained Benchmark for Video Captioning and Retrieval
Online Video Understanding: OVBench and VideoChat-Online
VideoRefer Suite: Advancing Spatial-Temporal Object Understanding with Video LLM
VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generation
Detection-Fusion for Knowledge Graph Extraction from Videos
FrameFusion: Combining Similarity and Importance for Video Token Reduction on Large Visual Language Models
ESVQA: Perceptual Quality Assessment of Egocentric Spatial Videos
Cross-Modal Fusion and Attention Mechanism for Weakly Supervised Video Anomaly Detection
ReTaKe: Reducing Temporal and Knowledge Redundancy for Long Video Understanding
MVTamperBench: Evaluating Robustness of Vision-Language Models
FineVQ: Fine-Grained User Generated Content Video Quality Assessment
Perceive, Query & Reason: Enhancing Video QA with Question-Guided Temporal Queries
TINQ: Temporal Inconsistency Guided Blind Video Quality Assessment
An Ensemble Approach to Short-form Video Quality Assessment Using Multimodal LLM
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