用深度学习自动识别海洋视频中的异常事件，助力生态监测。
Uncovering Anomalous Events for Marine Environmental Monitoring via Visual Anomaly Detection
- 基于深度网络的视觉异常检测，自动筛选水下异常片段。
- 在两个海洋场景中评估4种模型，性能受训练数据量和画面变化影响大。
- 提出软标签与共识标签策略，适合科研人员做生态调查。
水下视频监测是评估海洋生物多样性的有效手段，但海量无事件视频使人工检查难以实施。本文探索基于深度神经网络的视觉异常检测（VAD）技术，自动识别有趣或异常事件。提出AURA，首个用于水下VAD的多标注者基准数据集，并在两个海洋场景中评估四种VAD模型。研究揭示了鲁棒帧选择策略对提取有意义视频片段的重要性。与多位标注者对比发现，当前模型的VAD性能差异显著，且高度依赖训练数据量及“正常”场景的视觉内容多样性。结果表明软标签与共识标签具有价值，为科学探索和可扩展的生物多样性监测提供实用方案。
原文摘要 · Abstract (English)
Underwater video monitoring is a promising strategy for assessing marine biodiversity, but the vast volume of uneventful footage makes manual inspection highly impractical. In this work, we explore the use of visual anomaly detection (VAD) based on deep neural networks to automatically identify interesting or anomalous events. We introduce AURA, the first multi-annotator benchmark dataset for underwater VAD, and evaluate four VAD models across two marine scenes. We demonstrate the importance of robust frame selection strategies to extract meaningful video segments. Our comparison against multiple annotators reveals that VAD performance of current models varies dramatically and is highly sensitive to both the amount of training data and the variability in visual content that defines "normal" scenes. Our results highlight the value of soft and consensus labels and offer a practical approach for supporting scientific exploration and scalable biodiversity monitoring.
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