发表机构
Texas Tech University; Purdue University（德克萨斯理工大学; 普渡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CoRE是一种弱监督由粗到细框架，可从粗粒度视频监督中学习驾驶视频的细粒度风险证据，在三个基准测试中表现出色，无需耗时的细粒度标注即可实现风险定位。
AI 中文摘要
驾驶过程中感知风险会随时间演变，且可能由特定场景实体支撑，但监督信号通常仅局限于粗粒度的视频级判断。学习支撑证据出现的时机以及哪些实体支撑风险预测器，通常需要耗时的时间级和实体级标注。我们提出了**CoRE**，一种弱监督由粗到细框架，可从粗粒度视频监督中学习细粒度预测支撑。CoRE首先训练一个视频级预测器，随后将其冻结；对候选时间区域或实体轨迹进行结构化干预，测量每个候选对象如何改变粗粒度预测，从而产生分级的预测效果目标；将这些目标蒸馏到学生模型中，使其能直接从原始视频预测时间和实体支撑，无需在推理阶段进行干预。我们在三个互补设置中评估该学习原理：RISEE测试从主观片段级判断中获取感知风险支撑，无时间或实体级风险标注；DoTA提供独立的时间事件标注，用于评估弱监督交通异常定位；UCF-Crime测试同一由粗到细机制是否可扩展至标准非驾驶异常检测基准。在这些设置中，CoRE从粗粒度监督中学习到了有用的细粒度支撑，在DoTA上实现了出色的时间定位，在UCF-Crime上取得了有竞争力的性能。这些结果表明，粗粒度视频预测可提供有用的监督，用于恢复支撑它们的细粒度证据，无需相应的细粒度标签。
英文摘要
Perceived risk in driving evolves over time and may be supported by specific scene entities, yet supervision is typically limited to coarse video-level judgments. Learning \emph{when} supporting evidence emerges and \emph{which entities} support a risk predictor would ordinarily require costly temporal- and entity-level annotations. We introduce \textbf{CoRE}, a weakly supervised coarse-to-fine framework that learns fine-grained prediction support from coarse video supervision. CoRE first trains a video-level predictor and then freezes it. Structured interventions over candidate temporal regions or entity tracks measure how each candidate changes the coarse prediction, producing graded prediction-effect targets. These targets are distilled into a student that directly predicts temporal and entity support from the original video, without requiring interventions at inference. We evaluate this learning principle across three complementary settings: RISEE tests perceived-risk support from subjective clip-level judgments without temporal or entity-level risk annotations; DoTA provides independent temporal event annotations for evaluating weakly supervised traffic-anomaly localization; and UCF-Crime tests whether the same coarse-to-fine mechanism extends to a standard non-driving anomaly-detection benchmark. Across these settings, CoRE learns informative fine-grained support from coarse supervision, with strong temporal localization on DoTA and competitive performance on UCF-Crime. These results show that coarse video predictions can provide useful supervision for recovering the fine-grained evidence supporting them, without requiring corresponding fine-grained labels.
