  Most of the computer vision architectures nowadays are built upon the
well-known foundation operations: fully-connected layers, convolutions and
multi-head self-attention blocks. In this paper we propose a novel foundation
operation - NeoCell - which learns matrix patterns and performs patchwise
matrix multiplications with the input data. The main advantages of the proposed
operator are (1) simple implementation without need in operations like im2col,
(2) low computational complexity (especially for large matrices) and (3) simple
and flexible implementation of up-/down-sampling. We validate NeoNeXt family of
models based on this operation on ImageNet-1K classification task and show that
they achieve competitive quality.
