FViT: A Focal Vision Transformer with Gabor Filter

TL;DR

FViT introduces a novel vision transformer architecture that integrates learnable Gabor filters and biologically inspired modules to improve efficiency and performance in dense vision tasks.

Contribution

This paper proposes FViT, a new vision transformer framework combining Gabor filters and neuroscience-inspired blocks for better feature focus and reduced complexity.

Findings

FViT outperforms existing models in multiple vision tasks.

FViT demonstrates higher computational efficiency and scalability.

The biologically inspired design improves feature discrimination across scales.

Abstract

Vision transformers have achieved encouraging progress in various computer vision tasks. A common belief is that this is attributed to the capability of self-attention in modeling the global dependencies among feature tokens. However, self-attention still faces several challenges in dense prediction tasks, including high computational complexity and absence of desirable inductive bias. To alleviate these issues, the potential advantages of combining vision transformers with Gabor filters are revisited, and a learnable Gabor filter (LGF) using convolution is proposed. The LGF does not rely on self-attention, and it is used to simulate the response of fundamental cells in the biological visual system to the input images. This encourages vision transformers to focus on discriminative feature representations of targets across different scales and orientations. In addition, a Bionic Focal Vision (BFV) block is designed based on the LGF. This block draws inspiration from neuroscience and introduces a Dual-Path Feed Forward Network (DPFFN) to emulate the parallel and cascaded information processing scheme of the biological visual cortex. Furthermore, a unified and efficient family of pyramid backbone networks called Focal Vision Transformers (FViTs) is developed by stacking BFV blocks. Experimental results indicate that FViTs demonstrate superior performance in various vision tasks. In terms of computational efficiency and scalability, FViTs show significant advantages compared with other counterparts.