Can I read EViT: An Eagle Vision Transformer with Bi-Fovea Self-Attention on EtoBox?

EViT: An Eagle Vision Transformer with Bi-Fovea Self-Attention by Shi, Yulong; Sun, Mingwei; Wang, Yongshuai; Ma, Jiahao; Chen, Zengqiang is a scholarly article available to read on EtoBox.

What is EViT: An Eagle Vision Transformer with Bi-Fovea Self-Attention about?

Owing to advancements in deep learning technology, Vision Transformers (ViTs) have demonstrated impressive performance in various computer vision tasks. Nonetheless, ViTs still face some challenges, such as high computational complexity and the absence of desirable inductive biases. To alleviate these issues, {the potential advantages of combining eagle vision with ViTs are explored. We summarize a Bi-Fovea Visual Interaction (BFVI) structure inspired by the unique physiological and visual characteristics of eagle eyes. A novel Bi-Fovea Self-Attention (BFSA) mechanism and Bi-Fovea Feedforward Network (BFFN) are proposed based on this structural design approach, which can be used to mimic the hierarchical and parallel information processing scheme of the biological visual cortex, enabling networks to learn feature representations of targets in a coarse-to-fine manner. Furthermore, a Bionic Eagle Vision (BEV) block is designed as the basic building unit based on the BFSA mechanism and BFFN. By stacking BEV blocks, a unified and efficient family of pyramid backbone networks called Eagle Vision Transformers (EViTs) is developed. Experimental results show that EViTs exhibit highly compe

Author

Shi, Yulong; Sun, Mingwei; Wang, Yongshuai; Ma, Jiahao; Chen, Zengqiang

Published

2023

Language

EN