Papers › EViT: An Eagle Vision Transformer with Bi-Fovea Self-Attention

EViT: An Eagle Vision Transformer with Bi-Fovea Self-Attention

10 Oct 2023arXiv:2310.06629archive 2025-07-28

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

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 competitive performance in various computer vision tasks, such as image classification, object detection and semantic segmentation. Compared with other approaches, EViTs have significant advantages, especially in terms of performance and computational efficiency. Code is available at https://github.com/nkusyl/EViT

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Computational Efficiency Image Classification Inductive Bias Instance Segmentation Object Detection Semantic Segmentation Transfer Learning image-classification object-detection

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Absolute Position Encodings Adam Attention BPE Convolution Dense Connections Dropout Feedforward Network Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection SPEED Softmax Transformer Vision Transformer

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