Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Series Vol. 5 , 31 May 2023


Open Access | Article

Researches advanced in fine-grained image classification based on convolutional neural network

Shiyao Xu * 1
1 Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 5, 118-125
Published 31 May 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Shiyao Xu. Researches advanced in fine-grained image classification based on convolutional neural network. ACE (2023) Vol. 5: 118-125. DOI: 10.54254/2755-2721/5/20230545.

Abstract

Due to practical needs, fine-grained image classification (FGIC) has been considered for many years as a direction of study in computer vision, which aims to subdivide images belonging to one coarse-grained category into multiple fine-grained classes. Traditional fine-grained image classification algorithms rely heavily on annotations. Recently, convolutional neural networks (CNN) have prefigured unprecedented opportunities for this research direction with the popularity and development in deep learning. To start, this study introduces the development history with various fine-grained image classification algorithms, as well as definition and research significance of the problem. After that, it compares and analyzes the different algorithms respectively in the aspects of strong supervision and weak supervision. This paper also compares the accuracy of these models on frequently used datasets. We conclude the paper by summarizing and evaluating the different aspects of these algorithms, and then discuss possible future research domains and challenges in this field.

Keywords

Fine-grained Image Classification, Convolutional Neural Network, Weakly Supervised Learning

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-915371-57-7
ISBN (Online)
978-1-915371-58-4
Published Date
31 May 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/5/20230545
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated