Parser-Free VLM Verification for Federated Weakly Supervised Video Anomaly Detection
How can vision-language models help video anomaly detection (VAD) when surveillance data remain distributed, weakly labeled, and resource-constrained? Most weakly supervised VAD methods assume centralized training; recent VLM-based extensions further rely on dense inference, generated explanations, or additional adaptation. We introduce a lightweight federated MIL-VLM cascade in which only a compact MIL scorer is trained across clients, while a frozen VLM verifies high-scoring suspect segments post hoc. We study two VLM feedback interfaces: parsed text-generation decisions and a logit-based interface that extracts a continuous anomaly score from next-token Yes/No probabilities. Experiment...
| Authors | Sébastien Thuau, Amira Gran, Siba Haidar et al. |
|---|---|
| Primary category | cs.CV |
| Published | 7 September 2026 |
| Last updated at source | 7 September 2026 |
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