RiskCueBench: Benchmarking Anticipatory Reasoning from Early Risk Cues in Video-Language Models

2026-01-06

arXiv.org

AI Summary

Existing video-based risk assessment methods typically rely on videos containing complete accident sequences, making them ill-suited for real-world scenarios where hazardous events must be predicted from early risk cues alone. To address this limitation, this work proposes RiskCueBench—a novel benchmark focused explicitly on the earliest visual indicators of potential safety risks. By meticulously annotating the first segments in videos that signal emerging hazards, RiskCueBench establishes a forward-looking evaluation framework for video-language models centered on prospective risk reasoning. This benchmark uniquely emphasizes prediction based solely on initial visual cues, aligning more closely with practical deployment requirements, and spans diverse risk domains. Experimental results reveal a significant performance gap in current models’ ability to anticipate future risks from such early signals, underscoring both the challenge and the research value of this task.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• anticipatory reasoning
• early risk cues
• video-language models
• risk prediction
• public safety

Innovation

• anticipatory reasoning
• early risk cues
• video-language models
• risk prediction
• benchmark