  In recent years, the task of weakly supervised audio-visual violence
detection has gained considerable attention. The goal of this task is to
identify violent segments within multimodal data based on video-level labels.
Despite advances in this field, traditional Euclidean neural networks, which
have been used in prior research, encounter difficulties in capturing highly
discriminative representations due to limitations of the feature space. To
overcome this, we propose HyperVD, a novel framework that learns snippet
embeddings in hyperbolic space to improve model discrimination. Our framework
comprises a detour fusion module for multimodal fusion, effectively alleviating
modality inconsistency between audio and visual signals. Additionally, we
contribute two branches of fully hyperbolic graph convolutional networks that
excavate feature similarities and temporal relationships among snippets in
hyperbolic space. By learning snippet representations in this space, the
framework effectively learns semantic discrepancies between violent and normal
events. Extensive experiments on the XD-Violence benchmark demonstrate that our
method outperforms state-of-the-art methods by a sizable margin.
