Audiovisual Dependency Attention for Violence Detection in Videos

Violence detection in videos can help maintain public order, detect crimes, or provide timely assistance. In this paper, we aim to leverage multimodal information to determine whether successive frames contain violence. Specifically, we propose an audiovisual dependency attention (AVD-attention) module modified from the co-attention architecture to fuse visual and audio information, unlike commonly used methods such as the feature concatenation, addition, and score fusion. Because the AVD-attention module's dependency map contains sufficient fusion information, we argue that it should be applied more sufficiently. A combination pooling method is utilized to convert the dependency map to an attention vector, which can be considered a new feature that includes fusion information or a mask of the attention feature map. Since some information in the input feature might be lost after processing by attention modules, we employ a multimodal low-rank bilinear method that considers all pairwise interactions among two features in each time step to complement the original information for output features of the module. AVD-attention outperformed co-attention in experiments on the XD-Violence dataset. Our system outperforms state-of-the-art systems.