Vision transformers have achieved encouraging progress in various computer
vision tasks. A common belief is that this is attributed to the competence of
self-attention in modeling the global dependencies among feature tokens.
Unfortunately, self-attention still faces some challenges in dense prediction
tasks, such as the high computational complexity and absence of desirable
inductive bias. To address these above issues, we revisit the potential
benefits of integrating vision transformer with Gabor filter, and propose a
Learnable Gabor Filter (LGF) by using convolution. As an alternative to
self-attention, we employ LGF to simulate the response of simple cells in the
biological visual system to input images, prompting models to focus on
discriminative feature representations of targets from various scales and
orientations. Additionally, we designed a Bionic Focal Vision (BFV) block based
on the LGF. This block draws inspiration from neuroscience and introduces a
Multi-Path Feed Forward Network (MPFFN) to emulate the working way of
biological visual cortex processing information in parallel. Furthermore, we
develop a unified and efficient pyramid backbone network family called Focal
Vision Transformers (FViTs) by stacking BFV blocks. Experimental results show
that FViTs exhibit highly competitive performance in various vision tasks.
Especially in terms of computational efficiency and scalability, FViTs show
significantly advantages compared with other counterparts.

利用 Gabor 滤波器和学习能力，结合视觉变换器模型，设计了一种有效的金字塔网络结构，称为 Focal Vision Transformers（FViTs），在各种视觉任务中展现出出色的性能。

FViT：一种具有 Gabor 滤波器的聚焦视觉变换器

FViT: A Focal Vision Transformer with Gabor Filter

Convolutional Neural Networks (CNN) have been used in Automatic Speech
Recognition (ASR) to learn representations directly from the raw signal instead
of hand-crafted acoustic features, providing a richer and lossless input
signal. Recent researches propose to inject prior acoustic knowledge to the
first convolutional layer by integrating the shape of the impulse responses in
order to increase both the interpretability of the learnt acoustic model, and
its performances. We propose to combine the complex Gabor filter with
complex-valued deep neural networks to replace usual CNN weights kernels, to
fully take advantage of its optimal time-frequency resolution and of the
complex domain. The conducted experiments on the TIMIT phoneme recognition task
shows that the proposed approach reaches top-of-the-line performances while
remaining interpretable.

使用复杂的 Gabor 滤波器和深度神经网络的方法，在 TIMIT 音素识别任务中取得了最佳表现，同时保持可解释性。