Recently, the proposed deep multilayer perceptron (MLP) models have stirred up a lot of interest in the vision community. Historically, the availability of larger datasets combined with increased computing capacity led to paradigm shifts. This review provides detailed discussions on whether MLPs can be a new paradigm for computer vision. We compare the intrinsic connections and differences between convolution, self-attention mechanism, and token-mixing MLP in detail. Advantages and limitations of token-mixing MLP are provided, followed by careful analysis of recent MLP-like variants, from module design to network architecture, and their applications. In the graphics processing unit era, the locally and globally weighted summations are the current mainstreams, represented by the convolution and self-attention mechanism, as well as MLPs. We suggest the further development of the paradigm to be considered alongside the next-generation computing devices.
Keywords: computer vision; deep MLP; neural network; paradigm shift.
© 2022 The Author(s).