  Video anomaly detection under weak supervision presents significant
challenges, particularly due to the lack of frame-level annotations during
training. While prior research has utilized graph convolution networks and
self-attention mechanisms alongside multiple instance learning (MIL)-based
classification loss to model temporal relations and learn discriminative
features, these methods often employ multi-branch architectures to capture
local and global dependencies separately, resulting in increased parameters and
computational costs. Moreover, the coarse-grained interclass separability
provided by the binary constraint of MIL-based loss neglects the fine-grained
discriminability within anomalous classes. In response, this paper introduces a
weakly supervised anomaly detection framework that focuses on efficient context
modeling and enhanced semantic discriminability. We present a Temporal Context
Aggregation (TCA) module that captures comprehensive contextual information by
reusing the similarity matrix and implementing adaptive fusion. Additionally,
we propose a Prompt-Enhanced Learning (PEL) module that integrates semantic
priors using knowledge-based prompts to boost the discriminative capacity of
context features while ensuring separability between anomaly sub-classes.
Extensive experiments validate the effectiveness of our method's components,
demonstrating competitive performance with reduced parameters and computational
effort on three challenging benchmarks: UCF-Crime, XD-Violence, and
ShanghaiTech datasets. Notably, our approach significantly improves the
detection accuracy of certain anomaly sub-classes, underscoring its practical
value and efficacy. Our code is available at:
https://github.com/yujiangpu20/PEL4VAD.
