Rethink Cross-Modal Fusion in Weakly-Supervised Audio-Visual Video Parsing

• URL: http://arxiv.org/abs/2311.08151v1
• Date: Tue, 14 Nov 2023 13:27:03 GMT
• Title: Rethink Cross-Modal Fusion in Weakly-Supervised Audio-Visual Video Parsing
• Authors: Yating Xu, Conghui Hu, Gim Hee Lee
• Abstract summary: We propose a messenger-guided mid-fusion transformer to reduce the uncorrelated cross-modal context in the fusion. The messengers condense the full cross-modal context into a compact representation to only preserve useful cross-modal information. We thus propose cross-audio prediction consistency to suppress the impact of irrelevant audio information on visual event prediction.
• Score: 58.9467115916639
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing works on weakly-supervised audio-visual video parsing adopt hybrid attention network (HAN) as the multi-modal embedding to capture the cross-modal context. It embeds the audio and visual modalities with a shared network, where the cross-attention is performed at the input. However, such an early fusion method highly entangles the two non-fully correlated modalities and leads to sub-optimal performance in detecting single-modality events. To deal with this problem, we propose the messenger-guided mid-fusion transformer to reduce the uncorrelated cross-modal context in the fusion. The messengers condense the full cross-modal context into a compact representation to only preserve useful cross-modal information. Furthermore, due to the fact that microphones capture audio events from all directions, while cameras only record visual events within a restricted field of view, there is a more frequent occurrence of unaligned cross-modal context from audio for visual event predictions. We thus propose cross-audio prediction consistency to suppress the impact of irrelevant audio information on visual event prediction. Experiments consistently illustrate the superior performance of our framework compared to existing state-of-the-art methods.

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