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cs.CV2026
TD-VAD: Breaking Visual Dependence in Video Anomaly Detection with Text-Driven Learning
Shuangqing Zhang, Lei-Lei Ma, Zhao Wang +5
Visual data is typically a prerequisite for training existing video anomaly detection (VAD) methods. However, obtaining sufficient annotated anomaly data for training is challengin…
cs.CV2026
Global Logic and Local Search: Dual-Stream Multimodal In-Context Learning for Verifiable Industrial Anomaly Detection
Runzhi Deng, Yundi Hu, Yiming Zhong +5
Large Multimodal Models (LMMs) show strong few-shot generalization, but industrial anomaly detection remains difficult because defects are small, input resolution is limited, and t…
cs.CV2026
CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection
Wen Dong, Zhao Wang, Shuangqing Zhang +5
Multimodal Large Language Models (MLLMs) excel in diverse vision tasks, but full-parameter retraining is computationally expensive as real-world knowledge evolves. Existing continu…