A Review of Fine-Grained Image Recognition Techniques Based on Deep Learning

Zhexuan Dong

2025

Abstract

Fine-grained image recognition aims to visually recognize different subcategories of traditional semantic categories in images at a fine-grained level. It holds significant scientific value and promising application prospects across various fields such as biological classification, security monitoring, smart retail, medical diagnosis, and industrial manufacturing. Although fine-grained image recognition has achieved significant results with the support of deep learning methods, its dependence on large-scale, high-quality fine-grained image data has become the main bottleneck limiting the promotion and popularization of this technology. This paper focuses on fine-grained image recognition, introduces the relevant classical public data sets in five fields, and then introduces the FGIR method based on strong supervision and the FGIR method based on weak supervision. It is concluded that the choice of backbone network has a significant impact on the performance of fine-grained image recognition. Finally, the emerging trends and future directions of fine-grained image recognition are analyzed and concluded.

Paper Citation

in Harvard Style

Dong Z. (2025). A Review of Fine-Grained Image Recognition Techniques Based on Deep Learning. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 188-193. DOI: 10.5220/0013680800004670

in Bibtex Style

@conference{icdse25,
author={Zhexuan Dong},
title={A Review of Fine-Grained Image Recognition Techniques Based on Deep Learning},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={188-193},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013680800004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - A Review of Fine-Grained Image Recognition Techniques Based on Deep Learning
SN - 978-989-758-765-8
AU - Dong Z.
PY - 2025
SP - 188
EP - 193
DO - 10.5220/0013680800004670
PB - SciTePress