iSafetyBench: A video-language benchmark for safety in industrial environment

Recent advances in vision-language models (VLMs) have enabled impressive generalization across diverse video understanding tasks under zero-shot settings. However, their capabilities in high-stakes industrial domains—where recognizing both routine operations and safety-critical anomalies is essential—remain largely underexplored. To address this gap, we introduce iSafetyBench, a new video-language benchmark specifically designed to evaluate model performance in industrial environments across both normal and hazardous scenarios. iSafetyBench comprises 1,100 video clipssourced from real-world industrial settings, annotated with open-vocabulary, multi-label action tagsspanning 98 routine and 67 hazardousaction categories. Each clip is paired with multiple-choice questionsfor both single-label and multi-label evaluation, enabling fine-grained assessment of VLMs in both standard and safety-critical contexts. We evaluate eight state-of-the-art video-language modelsunder zero-shot conditions. Despite their strong performance on existing video benchmarks, these models struggle with iSafetyBench—particularly in recognizing hazardous activities and in multi-label scenarios. Our results reveal significant performance gaps, underscoring the need for more robust, safety-aware multimodal models for industrial applications. iSafetyBench provides a first-of-its-kind testbed to drive progress in this direction.