RiskCueBench: 视频语言模型中早期风险信号的前瞻性推理基准测试
RiskCueBench: Benchmarking Anticipatory Reasoning from Early Risk Cues in Video-Language Models
AI总结：
RiskCueBench通过标注早期风险信号片段，评估视频语言模型在预测未来风险事件中的能力，揭示了现有系统在解读动态情境方面的不足。
摘要：
随着以视频为中心的社会媒体迅速发展，从视觉数据中预测风险事件的能力成为确保公共安全和防止现实世界事故的有前途的方向。先前的工作已经广泛研究了监督视频风险评估，涉及驾驶、抗议和自然灾害等多个领域。然而，许多现有数据集为模型提供完整视频序列的访问权限，包括事故本身，这大大降低了任务的难度。为了更好地反映现实世界条件，我们引入了一个新的视频理解基准测试RiskCueBench，其中视频被精心标注以识别风险信号片段，定义为最早表明潜在安全问题的时刻。实验结果揭示了当前系统在解读演变情况和从早期视觉信号中预测未来风险事件方面存在显著差距，突显了在实践中部署视频风险预测模型的重要挑战。
英文摘要：
With the rapid growth of video centered social media, the ability to anticipate risky events from visual data is a promising direction for ensuring public safety and preventing real world accidents. Prior work has extensively studied supervised video risk assessment across domains such as driving, protests, and natural disasters. However, many existing datasets provide models with access to the full video sequence, including the accident itself, which substantially reduces the difficulty of the task. To better reflect real world conditions, we introduce a new video understanding benchmark RiskCueBench in which videos are carefully annotated to identify a risk signal clip, defined as the earliest moment that indicates a potential safety concern. Experimental results reveal a significant gap in current systems ability to interpret evolving situations and anticipate future risky events from early visual signals, highlighting important challenges for deploying video risk prediction models in practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
