  The vanilla fusion methods still dominate a large percentage of mainstream
audio-visual tasks. However, the effectiveness of vanilla fusion from a
theoretical perspective is still worth discussing. Thus, this paper reconsiders
the signal fused in the multimodal case from a bionics perspective and proposes
a simple, plug-and-play, attention module for vanilla fusion based on
fundamental signal theory and uncertainty theory. In addition, previous work on
multimodal dynamic gradient modulation still relies on decoupling the
modalities. So, a decoupling-free gradient modulation scheme has been designed
in conjunction with the aforementioned attention module, which has various
advantages over the decoupled one. Experiment results show that just a few
lines of code can achieve up to 2.0% performance improvements to several
multimodal classification methods. Finally, quantitative evaluation of other
fusion tasks reveals the potential for additional application scenarios.
