VBNet: A Visually-Aware Biomimetic Network for Simulating the Human Eye’s Visual System

Abstract

ABSTRACT In the rapidly advancing realms of computer vision and artificial intelligence, the quest for human-like intelligence is escalating. Central to this pursuit is visual perception, with the human eye as a paragon of efficiency in the natural world. Recent research has prominently embraced the emulation of the human eye’s visual system in computer vision. This paper introduces a pioneering approach, the visually-aware biomimetic network (VBNet), composed of a dual-branch parallel architecture: a Transformer branch emulating the peripheral retina for global feature dependencies and a CNN branch resembling the macular region for local details. Furthermore, it employs feature converter modules (CFC and TFC) to enhance information fusion between the branches. Empirical results highlight VBNet’s superiority over RegNet and PVT in ImageNet classification and competitive performance in MSCOCO object detection and instance segmentation. The dual-branch design, akin to the human visual system, enables simultaneous focus on local and global features, offering fresh perspectives for future research in the field of computer vision and artificial intelligence.