Multi-level Attention Fusion Network for Audio-visual Event Recognition
This work addresses event classification in videos for applications like surveillance or content analysis, but it appears incremental as it builds on existing attention and fusion methods.
The authors tackled audio-visual event recognition by proposing a Multi-level Attention Fusion network (MAFnet) that dynamically fuses visual and audio information, which improved accuracy on datasets like AVE, UCF51, and Kinetics-Sounds.
Event classification is inherently sequential and multimodal. Therefore, deep neural models need to dynamically focus on the most relevant time window and/or modality of a video. In this study, we propose the Multi-level Attention Fusion network (MAFnet), an architecture that can dynamically fuse visual and audio information for event recognition. Inspired by prior studies in neuroscience, we couple both modalities at different levels of visual and audio paths. Furthermore, the network dynamically highlights a modality at a given time window relevant to classify events. Experimental results in AVE (Audio-Visual Event), UCF51, and Kinetics-Sounds datasets show that the approach can effectively improve the accuracy in audio-visual event classification. Code is available at: https://github.com/numediart/MAFnet