Because of the advancement of deep learning technology, vision transformer has demonstrated competitive performance in various computer vision tasks. Unfortunately, vision transformer still faces some challenges such as high computational complexity and absence of desirable inductive bias. To alleviate these problems, this study proposes a novel Bi-Fovea Self-Attention (BFSA) inspired by the physiological structure and characteristics of bi-fovea vision in eagle eyes. This BFSA can simulate the shallow fovea and deep fovea functions of eagle vision, enabling the network to extract feature representations of targets from coarse to fine, facilitating the interaction of multi-scale feature representations. Additionally, this study designs a Bionic Eagle Vision (BEV) block based on BFSA and CNN. It combines CNN and Vision Transformer, to enhance the network's local and global representation ability for targets. Furthermore, this study develops a unified and efficient general pyramid backbone network family, named Eagle Vision Transformers (EViTs) by stacking the BEV blocks. Experimental results on various computer vision tasks including image classification, object detection, instance segmentation and other transfer learning tasks show that the proposed EViTs perform significantly better than the baselines under similar model sizes, which exhibits faster speed on graphics processing unit compared to other models. Code will be released at https://github.com/nkusyl.

翻译：由于深度学习技术的进步，视觉Transformer已在多种计算机视觉任务中展现出竞争性性能。然而，视觉Transformer仍面临计算复杂度高和缺乏理想归纳偏置等挑战。为缓解这些问题，本研究受鹰眼双凹生理结构与特性的启发，提出了一种新型双凹自注意力机制（BFSA）。该BFSA能够模拟鹰眼浅凹与深凹功能，使网络从粗到细提取目标特征表示，促进多尺度特征表示的交互。此外，本研究基于BFSA和CNN设计了一种仿鹰眼视觉（BEV）模块。该模块融合CNN与视觉Transformer，增强网络对目标的局部与全局表示能力。进一步地，通过堆叠BEV模块，本研究开发了一个统一高效的通用的金字塔骨干网络家族——鹰眼视觉Transformer（EViTs）。在图像分类、目标检测、实例分割及其他迁移学习任务等多种计算机视觉任务上的实验结果表明，在相似模型规模下，所提出的EViTs相比基线方法性能显著更优，且在图形处理器上运行速度相较于其他模型更快。代码将发布在https://github.com/nkusyl。