论文标题
局部意识的注意网络，具有判别性动态学习，用于弱监督异常检测
Locality-aware Attention Network with Discriminative Dynamics Learning for Weakly Supervised Anomaly Detection
论文作者
论文摘要
视频异常检测最近被作为在弱监督下作为多个实例学习任务制定的，其中每个视频都被视为要确定是否包含异常的片段。先前的努力主要集中于摘要本身的歧视，而不对时间动力进行建模，这是指相邻摘要的变化。因此，我们提出了一种具有两个目标函数的歧视性动力学学习（DDL）方法，即动态排名损耗和动态对齐损失。前者的目标是扩大正面和负袋之间的得分动态差距，而后者则在袋中进行特征动力和得分动态的时间对齐。此外，构建了一个局部感知的注意力网络（LA-NET），以捕获全局相关性并重新校准跨摘要的位置偏好，然后是带有因果卷积的多层感知器以获得异常得分。实验结果表明，我们的方法在两个具有挑战性的基准（即UCF-Crime和XD-Violence）上取得了重大改进。
Video anomaly detection is recently formulated as a multiple instance learning task under weak supervision, in which each video is treated as a bag of snippets to be determined whether contains anomalies. Previous efforts mainly focus on the discrimination of the snippet itself without modeling the temporal dynamics, which refers to the variation of adjacent snippets. Therefore, we propose a Discriminative Dynamics Learning (DDL) method with two objective functions, i.e., dynamics ranking loss and dynamics alignment loss. The former aims to enlarge the score dynamics gap between positive and negative bags while the latter performs temporal alignment of the feature dynamics and score dynamics within the bag. Moreover, a Locality-aware Attention Network (LA-Net) is constructed to capture global correlations and re-calibrate the location preference across snippets, followed by a multilayer perceptron with causal convolution to obtain anomaly scores. Experimental results show that our method achieves significant improvements on two challenging benchmarks, i.e., UCF-Crime and XD-Violence.
