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The vanilla fusion methods still dominate a large percentage of mainstreamaudio-visual tasks. However, the effectiveness of vanilla fusion from atheoretical perspective is still worth discussing. Thus, this paper reconsidersthe signal fused in the multimodal case from a bionics perspective and proposesa simple, plug-and-play, attention module for vanilla fusion based onfundamental signal theory and uncertainty theory. In addition, previous work onmultimodal dynamic gradient modulation still relies on decoupling themodalities. So, a decoupling-free gradient modulation scheme has been designedin conjunction with the aforementioned attention module, which has variousadvantages over the decoupled one. Experiment results show that just a fewlines of code can achieve up to 2.0% performance improvements to severalmultimodal classification methods. Finally, quantitative evaluation of otherfusion tasks reveals the potential for additional application scenarios.

More than Vanilla Fusion: a Simple, Decoupling-free, Attention Module for Multimodal Fusion Based on Signal Theory