少有人走的路：弱监督视频异常检测中的片段探索
The Road Less Seen: Segment Exploration for Weakly Supervised Video Anomaly Detection
针对弱监督视频异常检测中AUROC高但低FPR下召回率低的问题，提出结合时序聚类与不确定性采样的双重探索策略，可复用于其他弱监督时序定位任务中缓解正负样本极度不平衡。
原文依据 we propose a novel dual exploration strategy that combines temporal clustering with uncertainty-based segment exploration
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论弱监督学习（WSL）为视频异常检测（VAD）提供了一种经济高效的学习范式，它利用视频级标注而非昂贵的细粒度片段级标注。尽管当代方法在具有挑战性的真实世界监控视频上取得了有希望的结果，但大多数方法使用接收者操作特征曲线下面积（AUROC）进行评估。我们揭示，高AUROC可能导致在具有实际意义的假阳性率（FPR）阈值下召回率非常低。因此，这些模型的实际价值有限，尤其是在高风险领域（例如公共安全和医学诊断），漏报真实异常会带来高昂代价。这一令人惊讶的现象根源于弱监督与正常和异常视频片段之间高度不平衡分布之间的相互作用。为了解决VAD系统中的这一关键挑战，我们提出了一种新颖的双重探索策略，该策略结合了时序聚类与基于不确定性的片段探索。时序聚类基于语义和时序相似性选择多样化的片段，而基于不确定性的采样则针对模型不确定性高的低分片段。探索的主要目标是确保模型从广泛模式（包括多样和模糊的模式）中学习，从而做出更明智和稳健的决策，并减少假阴性。同时，我们采用两个实用指标来替代常用的AUROC分数，以进行更有效的评估。在具有挑战性的真实世界视频上进行的实验表明，与基线相比，我们的探索策略在这些指标上提高了VAD性能，这证明了其在真实世界设置中具有更高的实用价值。
英文原文摘要 1805 字符
Weakly supervised learning (WSL) provides a cost-effective learning paradigm for video anomaly detection (VAD) from data with video-level annotation instead of requiring costly fine-grained segment-level annotation. Although contemporary methods have shown promising results on challenging real-world surveillance videos, most of them are evaluated using the Area Under the Receiver Operating Characteristic Curve (AUROC). We reveal that a high AUROC could result in a very low recall for meaningful False Positive Rate (FPR) thresholds. Thus, these models suffer from limited practical values, especially in high-stake domains (e.g. public safety and medical diagnosis), where missing the true anomalies incur high cost. This surprising phenomenon is rooted in the interplay of weak supervision and the highly imbalanced distribution between normal and anomalous video segments. To tackle this key challenge in VAD systems, we propose a novel dual exploration strategy that combines temporal clustering with uncertainty-based segment exploration. Temporal clustering selects diverse segments based on both semantic and temporal similarity, while uncertainty-based sampling targets low-scoring segments with high model uncertainty. The main aim of exploration is to ensure that the model learns from a wide range of patterns, both diverse and ambiguous, resulting in more informed and robust decision-making, and reduction in false negatives. Meanwhile, we adopt two practical metrics to replace the commonly used AUROC score for a more effective measure for evaluation. Experiments conducted in challenging real-world videos demonstrate that our exploration strategy improves VAD performance compared to the baselines on these metrics, which justifies its improved practical value in real-world settings.