The goal of weakly supervised video anomaly detection is to learn a detection
model using only video-level labeled data. However, prior studies typically
divide videos into fixed-length segments without considering the complexity or
duration of anomalies. Moreover, these studies usually just detect the most
abnormal segments, potentially overlooking the completeness of anomalies. To
address these limitations, we propose a Dynamic Erasing Network (DE-Net) for
weakly supervised video anomaly detection, which learns multi-scale temporal
features. Specifically, to handle duration variations of abnormal events, we
first propose a multi-scale temporal modeling module, capable of extracting
features from segments of varying lengths and capturing both local and global
visual information across different temporal scales. Then, we design a dynamic
erasing strategy, which dynamically assesses the completeness of the detected
anomalies and erases prominent abnormal segments in order to encourage the
model to discover gentle abnormal segments in a video. The proposed method
obtains favorable performance compared to several state-of-the-art approaches
on three datasets: XD-Violence, TAD, and UCF-Crime. Code will be made available
at this https URL

弱监督视频异常检测中，提出了一种动态擦除网络（DE-Net），能够学习多尺度时空特征，处理异常事件时长变化并评估异常检测的完整性。该方法在 XD-Violence、TAD 和 UCF-Crime 三个数据集上表现良好。

基于多尺度时域特征的动态擦除网络用于弱监督视频异常检测

Dynamic Erasing Network Based on Multi-Scale Temporal Features for  Weakly Supervised Video Anomaly Detection

We present PAT, a transformer-based network that learns complex temporal
co-occurrence action dependencies in a video by exploiting multi-scale temporal
features. In existing methods, the self-attention mechanism in transformers
loses the temporal positional information, which is essential for robust action
detection. To address this issue, we (i) embed relative positional encoding in
the self-attention mechanism and (ii) exploit multi-scale temporal
relationships by designing a novel non hierarchical network, in contrast to the
recent transformer-based approaches that use a hierarchical structure. We argue
that joining the self-attention mechanism with multiple sub-sampling processes
in the hierarchical approaches results in increased loss of positional
information. We evaluate the performance of our proposed approach on two
challenging dense multi-label benchmark datasets, and show that PAT improves
the current state-of-the-art result by 1.1% and 0.6% mAP on the Charades and
MultiTHUMOS datasets, respectively, thereby achieving the new state-of-the-art
mAP at 26.5% and 44.6%, respectively. We also perform extensive ablation
studies to examine the impact of the different components of our proposed
network.

我们提出了 PAT，一种基于 Transformer 的网络，通过利用多尺度时间特征来学习视频中复杂的时间共现动作依赖关系。