标题： iSafetyBench：工业环境安全的视频-语言基准 显示英文标题

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

摘要： 视觉语言模型（VLMs）的最新进展使得在零样本设置下，跨多种视频理解任务实现了令人印象深刻的泛化能力。 然而，在高风险工业领域——在这些领域中，识别常规操作和安全关键异常情况至关重要——它们的能力仍大多未被探索。 为解决这一差距，我们引入了iSafetyBench，这是一个新的视频语言基准，专门设计用于评估工业环境中模型在正常和危险情景下的性能。 iSafetyBench包含1,100个来自现实工业环境的视频片段，用开放词汇、多标签动作标签进行标注，涵盖98个常规动作和67个危险动作类别。 每个片段都配有单标签和多标签评估的多项选择题，能够对VLMs在标准和安全关键情境下的表现进行细粒度评估。 我们在零样本条件下评估了八种最先进的视频语言模型。 尽管它们在现有的视频基准测试中表现出色，但这些模型在iSafetyBench上表现不佳，特别是在识别危险活动和多标签场景方面。 我们的结果揭示了显著的性能差距，强调了在工业应用中需要更稳健、安全感知的多模态模型。 iSafetyBench提供了一个首创的测试平台，以推动这一方向的发展。 数据集可在以下位置获取：https://github.com/iSafetyBench/data. 显示英文摘要

摘要： 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 clips sourced from real-world industrial settings, annotated with open-vocabulary, multi-label action tags spanning 98 routine and 67 hazardous action categories. Each clip is paired with multiple-choice questions for 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 models under 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. The dataset is available at: https://github.com/iSafetyBench/data.