Studies Advanced in Weakly Supervised Fine-grained Image Classification based on Deep Learning

Research Article
Open access

Studies Advanced in Weakly Supervised Fine-grained Image Classification based on Deep Learning

Zhuoxi Chen 1*
  • 1 Chongqing Jiaotong University    
  • *corresponding author 632005010109@mails.cqjtu.edu.cn
Published on 1 August 2023 | https://doi.org/10.54254/2755-2721/8/20230256
ACE Vol.8
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-63-8
ISBN (Online): 978-1-915371-64-5

Abstract

Recently, the concept of fine-grained classification has arouse much attention., which has aroused heated disscussion of academia and industry. The extraction of picture characteristics in early efforts on fine-grained image classification relied on dense annotations, but acquiring these annotations was time-consuming and labor-intensive. Lately, weakly supervised fine-grained image classification has gradually emerged, which can mainly be separated between techniques using the attention mechanism and techniques using various neural networks. In this paper, focusing on the above two types of frameworks, we first introduce representative algorithms, including their innovation, basic processes, advantages and disadvantages. We then quantitatively compare the results of different algorithms on mainstream datasets, where the attention based methods can achieve excellent accuracy. We finally discuss the existing challenges and future development of the fine-grained classification task, which we believe can provide some new insight for this task.

Keywords:

fine-grained classification, weakly supervised, attention mechanism, convolution neural networks

Chen,Z. (2023). Studies Advanced in Weakly Supervised Fine-grained Image Classification based on Deep Learning. Applied and Computational Engineering,8,488-493.
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References

[1]. Chen P, Wei W 2022. J. Fine-Grained Image Classification based on Self-attention Feature Fusion and Graph-Propagation Phys. Conf. Ser. 2246 012067

[2]. Ridnik, T. et al. ML-Decoder: Scalable and Versatile Classification Head. 2021. Proc of Int Conf. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (2021): 32-41.

[3]. Xiao T, et al. 2014. Proc of Int Conf. The application of two-level attention models in deep convolutional neural network for fine-grained image classification. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014): 842-850.

[4]. Liu X, et al. 2016. J. Fully Convolutional Attention Localization Networks: Efficient Attention Localization for Fine-Grained Recognition. ArXiv abs/1603.06765.

[5]. Wang D, et al. 2015. Proc of Int Conf. Multiple Granularity Descriptors for Fine-Grained Categorization. IEEE International Conference on Computer Vision (ICCV) (2015): 2399-2406.

[6]. Fu J, et al. 2017. Proc of Int Conf. Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition.IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017): 4476-4484.

[7]. Sun, Ming et al. 2018. J. Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition. ArXiv abs/1806.05372 (2018).

[8]. Ge Z, et al. 2015. Proc of Int Conf. Subset feature learning for fine-grained category classification. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2015): 46-52.

[9]. Zhang X, Xiong H, Zhou W, et al. 2016. Proc of Int Conf. Picking deep filter responses for fine-grained image recognition. IEEE Conference on Computer Vision and Pattern Recognition. 2016:1134-1142.

[10]. Peng Y, He X, Zhao J. 2017. J. Object-part attention model for fine-grained image classification. IEEE Transactions on Image Processing, 2017, PP(99):1-1.

[11]. T. Y. Lin, A. RoyChowdhury, S. Maji. 2015. Proc of Int Conf. Bilinear CNN models for fine-grained visual recognition. In Proceedings of IEEE International Conference on Computer Vision, IEEE, Santiago, Chile, pp.1449–1457, 2015.

Cite this article

Chen,Z. (2023). Studies Advanced in Weakly Supervised Fine-grained Image Classification based on Deep Learning. Applied and Computational Engineering,8,488-493.

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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About volume

Volume title: Proceedings of the 2023 International Conference on Software Engineering and Machine Learning

ISBN：978-1-915371-63-8(Print) / 978-1-915371-64-5(Online)
Editor：Anil Fernando, Marwan Omar
Conference website: http://www.confseml.org
Conference date: 19 April 2023
Series: Applied and Computational Engineering
Volume number: Vol.8
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Chen P, Wei W 2022. J. Fine-Grained Image Classification based on Self-attention Feature Fusion and Graph-Propagation Phys. Conf. Ser. 2246 012067

[2]. Ridnik, T. et al. ML-Decoder: Scalable and Versatile Classification Head. 2021. Proc of Int Conf. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (2021): 32-41.

[3]. Xiao T, et al. 2014. Proc of Int Conf. The application of two-level attention models in deep convolutional neural network for fine-grained image classification. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014): 842-850.

[4]. Liu X, et al. 2016. J. Fully Convolutional Attention Localization Networks: Efficient Attention Localization for Fine-Grained Recognition. ArXiv abs/1603.06765.

[5]. Wang D, et al. 2015. Proc of Int Conf. Multiple Granularity Descriptors for Fine-Grained Categorization. IEEE International Conference on Computer Vision (ICCV) (2015): 2399-2406.

[6]. Fu J, et al. 2017. Proc of Int Conf. Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition.IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017): 4476-4484.

[7]. Sun, Ming et al. 2018. J. Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition. ArXiv abs/1806.05372 (2018).

[8]. Ge Z, et al. 2015. Proc of Int Conf. Subset feature learning for fine-grained category classification. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2015): 46-52.

[9]. Zhang X, Xiong H, Zhou W, et al. 2016. Proc of Int Conf. Picking deep filter responses for fine-grained image recognition. IEEE Conference on Computer Vision and Pattern Recognition. 2016:1134-1142.

[10]. Peng Y, He X, Zhao J. 2017. J. Object-part attention model for fine-grained image classification. IEEE Transactions on Image Processing, 2017, PP(99):1-1.

[11]. T. Y. Lin, A. RoyChowdhury, S. Maji. 2015. Proc of Int Conf. Bilinear CNN models for fine-grained visual recognition. In Proceedings of IEEE International Conference on Computer Vision, IEEE, Santiago, Chile, pp.1449–1457, 2015.

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