Reinforcement Learning-based Mixture of Vision Transformers for Video
  Violence Recognition
Hamid Mohammadi Ehsan Nazerfard Tahereh Firoozi
Video violence recognition based on deep learning concerns accurate yet
scalable human violence recognition. Currently, most state-of-the-art video
violence recognition studies use CNN-based models to represent and categorize
videos. However, recent studies suggest that pre-trained transformers are more
accurate than CNN-based models on various video analysis benchmarks. Yet these
models are not thoroughly evaluated for video violence recognition. This paper
introduces a novel transformer-based Mixture of Experts (MoE) video violence
recognition system. Through an intelligent combination of large vision
transformers and efficient transformer architectures, the proposed system not
only takes advantage of the vision transformer architecture but also reduces
the cost of utilizing large vision transformers. The proposed architecture
maximizes violence recognition system accuracy while actively reducing
computational costs through a reinforcement learning-based router. The
empirical results show the proposed MoE architecture's superiority over
CNN-based models by achieving 92.4% accuracy on the RWF dataset.