aiqbench analysis
This paper introduces a benchmark for testing whether video-language models can spot early warning cues, the earliest frames that signal a potential hazard, before an accident occurs. Its key finding is that current systems struggle to interpret evolving scenes and anticipate risk from weak early signals, which is a more realistic test than models that see the full clip. For AI readers, the practical takeaway is that benchmark results on full-video understanding may overstate readiness for real-world risk prediction.
RiskCueBench: Benchmarking Anticipatory Reasoning from Early Risk Cues in Video-Language Models
arXiv:2601.03369v3 Announce Type: replace-cross Abstract: 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.
Abstract: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.
Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
arXiv:2601.03369 [cs.CV]
(or arXiv:2601.03369v3 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2601.03369 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 244 words