Work

Fine-Grained Image Recognition with Bio-Inspired Gradient-Aware Attention

Year: 2025

Type: article

Abstract: Fine-grained image recognition is one of the key tasks in the field of computer vision. However, due to subtle inter-class differences and significant intra-class differences, it still faces severe challenges. Conventional approaches often struggle with background interference and feature degradation. To address these issues, we draw inspiration from the human visual system, which adeptly focuses on discriminative regions, to propose a bio-inspired gradient-aware attention mechanism. Our method explicitly models gradient information to guide the attention, mimicking biological edge sensitivity, thereby enhancing the discrimination between global structures and local details. Experiments on the CUB-200-2011, iNaturalist2018, nabbirds and Stanford Cars datasets demonstrated the superiority of our method, achieving Top-1 accuracy rates of 92.9%, 90.5%, 93.1% and 95.1%, respectively.

Source: Biomimetics

DOI: 10.3390/biomimetics10120834

Authors: Bing Ma, Junyi Li, Zhengbei Jin, Wei Zhang, Xiaohui Song, Beibei Jin

Institutions: Henan Academy of Sciences, Henan Normal University, Sun Yat-sen University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,654

Funder: Ministry of Industry and Information Technology of the People's Republic of China