Deep Learning Based Fine‐Grained Image Classification: Recent Advances, Applications and Future Outlook
Ying Liu, Haibin Zhang, Xin Che, Weidong Zhang, Guojun Lu · IET Image Processing · 2025
ABSTRACT Fine‐grained image classification (FGIC) aims to distinguish visually similar categories by capturing subtle differences, yet the coexistence of large intra‐class variation and small inter‐class differences makes this task highly challenging. This paper provides a systematic review of recent deep learning‐based FGIC methods. According to the type of training data, existing approaches are categorized into four groups: (1) conventional models with large‐scale samples (strongly supervised, weakly supervised, semi‐supervised, and unsupervised); (2) few‐shot learning models for limited‐sample scenarios (e.g. meta‐learning and metric learning); (3) models leveraging external information, including multi‐modal and web‐sourced data; and (4) emerging diffusion‐based models. Representative algorithms in each category are summarized and analysed in terms of their advantages and limitations. The paper also reviews mainstream benchmark datasets and introduces a newly proposed application‐oriented dataset, CIIP‐TPID, to support real‐world tasks. Additionally, practical applications of FGIC in public security, medicine, and commerce are discussed. Finally, future research directions are outlined, including diffusion‐based data augmentation, advanced multi‐modal fusion, transformer architecture optimization, lightweight models for edge deployment, and robustness against noisy labels. This review provides a structured and up‐to‐date reference for researchers and practitioners in the field.