Video anomaly detection under weak supervision is challenging due to the
absence of frame-level annotations during the training phase. Previous work has
employed graph convolution networks or self-attention mechanisms to model
temporal relations, along with multiple instance learning (MIL)-based
classification loss to learn discriminative features. However, most of them
utilize multi-branches to capture local and global dependencies separately,
leading to increased parameters and computational cost. Furthermore, the
binarized constraint of the MIL-based loss only ensures coarse-grained
interclass separability, ignoring fine-grained discriminability within
anomalous classes. In this paper, we propose a weakly supervised anomaly
detection framework that emphasizes efficient context modeling and enhanced
semantic discriminability. To this end, we first construct a temporal context
aggregation (TCA) module that captures complete contextual information by
reusing similarity matrix and adaptive fusion. Additionally, we propose a
prompt-enhanced learning (PEL) module that incorporates semantic priors into
the model by utilizing knowledge-based prompts, aiming at enhancing the
discriminative capacity of context features while ensuring separability between
anomaly sub-classes. Furthermore, we introduce a score smoothing (SS) module in
the testing phase to suppress individual bias and reduce false alarms.
Extensive experiments demonstrate the effectiveness of various components of
our method, which achieves competitive performance with fewer parameters and
computational effort on three challenging benchmarks: the UCF-crime,
XD-violence, and ShanghaiTech datasets. The detection accuracy of some anomaly
sub-classes is also improved with a great margin.

本文提出了一种强调高效上下文建模和增强语义可区分性的弱监督视频异常检测框架，其中包括时间上下文聚合模块、语义先验增强学习模块和评分平滑模块， 实验结果表明该方法以更少的参数和计算成本在三个具有挑战性的数据集上实现了竞争性的性能，某些异常子类的检测准确率也得到了极大的提高。