Anomaly-Guided Observation and Two-Level Semantic Feedback for Training-Free Video Anomaly Analysis
cs.CV
Submitted: 2026-10-12
Updated: 2026-10-12
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- Qwen2.5-VL Technical Report
- VADER: Towards Causal Video Anomaly Understanding with Relation-Aware Large Language Models
- AgenticVAU: Multi-Agent Explore-Verify Reasoning for Video Anomaly Understanding
- VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding
- MuST-VAD: Mutual Structured Learning for Video Anomaly Detection
- CoReVAD: A Contextual Reasoning Framework for Training-Free Video Anomaly Detection
- A Unified Reasoning Framework for Holistic Zero-Shot Video Anomaly Analysis
- Learning to Watch: Active Video Anomaly Understanding via Interleaved Policy Optimization
- Learning Transferable Visual Models From Natural Language Supervision
- Real-world Anomaly Detection in Surveillance Videos
- Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
- InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling
- Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision
- PANDA: Towards Generalist Video Anomaly Detection via Agentic AI Engineer
- VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding
- Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any Granularity
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