Hazard or Anomaly? Evaluating VLMs for Understanding Dangers and Discrepancies
Modern safety-critical systems increasingly rely on human-robot interaction to reduce disaster risk and support decision-making during emergencies. Vision-Language Models (VLMs) are promising for these settings because they can interpret complex scenes and communicate safety-relevant information, but they still require careful evaluation to ensure reliable safety reasoning. In particular, current evaluations often frame danger recognition as a binary decision (Safe/Unsafe), making it unclear whether a model is identifying true physical hazards or merely reacting to unusual scene elements. We address this limitation by introducing an explicit distinction between hazard and anomaly, and by ...
| Authors | Murali Indukuri, Mohammad Eskandari, Sree Nitya Kollu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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