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Shuang-Qing Zhang

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Preprint Sep 2026

PARSEE-VAD: Efficient Training-Free Online Video Anomaly Detection via Proposition-Aware Reasoning and Streaming Evidence Escalation

Training-free online video anomaly detection (VAD) with frozen multimodal language models faces two coupled challenges: extracting reliable current-window semantics under causal and computational constraints, and maintaining temporal continuity without repeatedly transmitting high-dimensional history. Encoding history...

Ji Wang, Shuang-Qing Zhang, Guo-Sen Xie et al. · 0 citations

2026

Reconstructive Visual Tuning for Weakly Supervised Video Anomaly Detection

Weakly supervised video anomaly detection (WS-VAD) presents a significant challenge in security video surveillance, as it aims to accurately identify anomaly frames in untrimmed videos with only video-level supervision. Several recent studies exploit vision-language pre-training models, e.g., CLIP, to take advantage of...

Shuang-Qing Zhang, Wei Xu, Yu-Qi Fang et al. · IEEE Transactions on Informa... · 2 citations

#small language model Preprint Sep 2026

Probe-VAD: Ordinal Likelihood Probing for Training-Free Video Anomaly Detection

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.

Jia-Wei Gu, Qi-Lin Zhao, Teng-Kuo Guo et al. · 1 citation

Preprint Aug 2026

TD-VAD: Breaking Visual Dependence in Video Anomaly Detection with Text-Driven Learning

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.

Shuang-Qing Zhang, Lei-Lei Ma, Zhao Wang et al. · 2 citations