  In this paper we introduce CUE-Net, a novel architecture designed for
automated violence detection in video surveillance. As surveillance systems
become more prevalent due to technological advances and decreasing costs, the
challenge of efficiently monitoring vast amounts of video data has intensified.
CUE-Net addresses this challenge by combining spatial Cropping with an enhanced
version of the UniformerV2 architecture, integrating convolutional and
self-attention mechanisms alongside a novel Modified Efficient Additive
Attention mechanism (which reduces the quadratic time complexity of
self-attention) to effectively and efficiently identify violent activities.
This approach aims to overcome traditional challenges such as capturing distant
or partially obscured subjects within video frames. By focusing on both local
and global spatiotemporal features, CUE-Net achieves state-of-the-art
performance on the RWF-2000 and RLVS datasets, surpassing existing methods.
