  The attention mechanism is the main component of the transformer
architecture, and since its introduction, it has led to significant
advancements in deep learning that span many domains and multiple tasks. The
attention mechanism was utilized in computer vision as the Vision Transformer
ViT, and its usage has expanded into many tasks in the vision domain, such as
classification, segmentation, object detection, and image generation. While
this mechanism is very expressive and capable, it comes with the drawback of
being computationally expensive and requiring datasets of considerable size for
effective optimization. To address these shortcomings, many designs have been
proposed in the literature to reduce the computational burden and alleviate the
data size requirements. Examples of such attempts in the vision domain are the
MLP-Mixer, the Conv-Mixer, the Perciver-IO, and many more. This paper
introduces a new computational block as an alternative to the standard ViT
block that reduces the compute burdens by replacing the normal attention layers
with a Network in Network structure that enhances the static approach of the
MLP-Mixer with a dynamic system of learning an element-wise gating function by
a token mixing process. Extensive experimentation shows that the proposed
design provides better performance than the baseline architectures on multiple
datasets applied in the image classification task of the vision domain.
