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EATFormer: Improving Vision Transformer Inspired by Evolutionary Algorithm
April 22, 2024, 4:45 a.m. | Jiangning Zhang, Xiangtai Li, Yabiao Wang, Chengjie Wang, Yibo Yang, Yong Liu, Dacheng Tao
cs.CV updates on arXiv.org arxiv.org
Abstract: Motivated by biological evolution, this paper explains the rationality of Vision Transformer by analogy with the proven practical Evolutionary Algorithm (EA) and derives that both have consistent mathematical formulation. Then inspired by effective EA variants, we propose a novel pyramid EATFormer backbone that only contains the proposed \emph{EA-based Transformer} (EAT) block, which consists of three residual parts, i.e., \emph{Multi-Scale Region Aggregation} (MSRA), \emph{Global and Local Interaction} (GLI), and \emph{Feed-Forward Network} (FFN) modules, to model multi-scale, …
algorithm arxiv cs.cv cs.et improving transformer type vision
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