用自注意力机制学习视频自身正常特征，实现少样本异常检测
Transformer Based Self-Context Aware Prediction for Few-Shot Anomaly Detection in Videos
- 基于自注意力的变压器模型，利用前几帧正常特征预测后续帧
- 仅需少量正常帧即可训练，检测准确率优于现有方法
- 适合异常类型多变的视频场景，尤其适用于数据稀缺任务
视频异常检测因异常种类多样而具有挑战性。为此，本文提出一种基于变压器的少样本自上下文感知异常检测方法。利用视频中前几帧正常特征训练变压器，使其在自学习的上下文注意力下预测下一帧的正常特征。训练完成后，通过比较预测特征与实际特征判断当前帧是否异常。在多个标准数据集上的定性和定量实验表明，该方法优于现有技术。同时，研究验证了自上下文机制对性能提升的积极影响。
原文摘要 · Abstract (English)
Anomaly detection in videos is a challenging task as anomalies in different videos are of different kinds. Therefore, a promising way to approach video anomaly detection is by learning the non-anomalous nature of the video at hand. To this end, we propose a one-class few-shot learning driven transformer based approach for anomaly detection in videos that is self-context aware. Features from the first few consecutive non-anomalous frames in a video are used to train the transformer in predicting the non-anomalous feature of the subsequent frame. This takes place under the attention of a self-context learned from the input features themselves. After the learning, given a few previous frames, the video-specific transformer is used to infer if a frame is anomalous or not by comparing the feature predicted by it with the actual. The effectiveness of the proposed method with respect to the state-of-the-art is demonstrated through qualitative and quantitative results on different standard datasets. We also study the positive effect of the self-context used in our approach.
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