FViT: A Focal Vision Transformer with Gabor Filter

Mingwei Sun; Yongshuai Wang; Yulong Shi; Zengqiang Chen

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FViT: A Focal Vision Transformer with Gabor Filter

Yulong Shi, Mingwei Sun, Yongshuai Wang, Zengqiang Chen

Computer Vision and Pattern Recognition · Submitted 17 Feb 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Experimental results indicate that FViTs demonstrate superior performance in various vision tasks.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 8,079 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.09 · human participants 0.02 · controlled comparison 0.38 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.72 · mentions code or data availability 0.02.

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arXiv listing abstract v3. Results and the main claim are underlined; show every role.

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	Vision transformers have achieved encouraging progress in various computer vision tasks.	background 1.00	0.06	0.03	asserted 0.98
s1	A common belief is that this is attributed to the capability of self-attention in modeling the global dependencies among feature tokens.	background 0.97	0.05	0.02	asserted 0.49
s2	However, self-attention still faces several challenges in dense prediction tasks, including high computational complexity and absence of desirable inductive bias.	background 0.61	0.11	0.03	asserted 0.99
s3	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.	contribution 0.80	0.87	0.06	asserted 0.85
s4	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.	method 0.94	0.80	0.04	asserted 1.00
s5	This encourages vision transformers to focus on discriminative feature representations of targets across different scales and orientations.	interpretation 0.46	0.30	0.07	asserted 0.73
s6	In addition, a Bionic Focal Vision (BFV) block is designed based on the LGF.	method 0.69	0.93	0.03	asserted 1.00
s7	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.	method 0.81	0.88	0.03	asserted 1.00
s8	Furthermore, a unified and efficient family of pyramid backbone networks called Focal Vision Transformers (FViTs) is developed by stacking BFV blocks.	method 0.54	0.94	0.09	asserted 1.00
s9	Experimental results indicate that FViTs demonstrate superior performance in various vision tasks.	result 1.00	0.94	0.62	hedged possibility 0.55
s10	In terms of computational efficiency and scalability, FViTs show significant advantages compared with other counterparts.	result 0.99	0.91	0.19	asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.99.

What was examined, and what was not

sources read

arXiv listing abstract v3

not examined

full text
figures and tables
references and citing sentences
relations to other papers

levels run

P abstract

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S sections
T body sentences
C citations
R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original 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.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it

Cites it as background 1 paper

Many hybrid or hierarchical architectures have been invented that often encode positions differently [ref] .

LookHere: Vision Transformers with Directed Attention Generalize and Extrapolate · Background and Related Work · 1.00

What it cites · 9 sentences without a confident label

Cites as background 2 papers

ViT [ref] is a groundbreaking endeavor that brings the transformer architecture into the domain of vision tasks.

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale · Transformers for Vision · 0.98
Most of the research focuses on designing more efficient self-attention modules [ref] and better integrating the advantages of self-attention and convolution [ref] .

EViT: An Eagle Vision Transformer with Bi-Fovea Self-Attention · Transformers for Vision · 0.90

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