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Probe-VAD is proposed, an ordinal binary-probing framework that directly probes severity preferences from a frozen VLM, providing a simple interface for translating frozen VLM visual understanding into continuous, rank-sensitive anomaly scores without task-specific training or caption-based compression.
This work presents GLLS, a training-free framework for reference-guided multimodal in-context verification that combines a Global&Logic Stream, where SAM 3 extracts partially checkable visual facts, with a Fine-Grained&Actions Stream, where MCTS selects local evidence crops under a fixed budget.
This work proposes a novel Text-Driven Video Anomaly Detection (TD-VAD) approach, which utilizes video-like text descriptions with temporal characteristics generated by LLM to train a VAD model, without any reliance on target-domain anomaly data.
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