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cs.CV2026
AnomalyCraft-700K: Component-Level Controllable and Verifiable Synthetic Anomalies for Fine-Grained Video Anomaly Understanding
Yuzhou Long, Haodong Zhang, Yunpeng Yang +2
Progress in video anomaly understanding (VAU) has long been limited by inherent deficiencies of real-world anomaly videos, which are hard to collect and offer little control over t…
cs.CV2026
Rethinking Auxiliary Modalities in Multimodal Zero-shot Anomaly Detection: From Semantic Fusion to Conditional Modulation
Peng Wu, Xin Ge, Yujia Sun +1
Recent foundation model-based methods have endowed RGB images with strong zero-shot anomaly detection (ZSAD) through vision-language pretraining. However, RGB observations alone re…
cs.CV2026
Towards Video Anomaly Detection from Event Streams: A Baseline and Benchmark Datasets
Peng Wu, Yuting Yan, Guansong Pang +4
Event-based vision, characterized by low redundancy, focus on dynamic motion, and inherent privacy-preserving properties, naturally fits the demands of video anomaly detection (VAD…