Research map / Video / Video segmentation and tracking
Local Patterns Generalize Better for Novel Anomalies
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Proposes a video anomaly detection framework that identifies and models local patterns, which generalize better to unseen anomalies than global patterns.
Problem
Video anomaly detection must identify novel actions or events unseen during training; existing mainstream methods focus on global patterns with redundant details and generalize poorly to unseen samples.
Approach
Identify local patterns that generalize to novel samples and model their dynamics, instead of relying on global patterns.
Closest papers by meaning
- Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any Granularity (CVPR 2025 · similarity 0.88)
- Harnessing Large Language Models for Training-free Video Anomaly Detection (CVPR 2024 · similarity 0.88)
- Open-Vocabulary Video Anomaly Detection (CVPR 2024 · similarity 0.88)
- VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Tree (NeurIPS 2025 · similarity 0.88)
- Multi-Scale Video Anomaly Detection by Multi-Grained Spatio-Temporal Representation Learning (CVPR 2024 · similarity 0.88)
- Video Anomaly Detection with Motion and Appearance Guided Patch Diffusion Model (AAAI 2025 · similarity 0.88)
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