Abstract
Fine-grained visual classification is challenging due to subtle differences between sub-categories. Current popular methods usually leverage a single image and are designed by two main perspectives: feature representation learning and discriminative parts localization, while a few methods utilize pairwise images as input. However, it is difficult to learn representations discriminatively both across the images and across the categories, as well as to guarantee for accurate location of discriminative parts. In this paper, different from the existing methods, we argue to solve these difficulties from the perspective of contrastive learning and propose a novel Attentive Contrast Learning Network (ACLN). The network aims to attract the representation of positive pairs, which are from the same category, and repulse the representation of negative pairs, which are from different categories. A contrastive learning module, equipped with two contrastive losses, is proposed to achieve this. Specifically, the attention maps, generated by the attention generator, are bounded with the original CNN feature as positive pair, while the attention maps of different images form the negative pairs. Besides, the final classification results are obtained by a synergic learning module, utilizing both the original feature and the attention maps. Comprehensive experiments are conducted on four benchmark datasets, on which our ACLN outperforms all the existing SOTA approaches. For reproducible scientific research https://github.com/mpskex/AttentiveContrastiveLearningNetwork.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. arXiv preprint arXiv:2002.05709 (2020)
Chen, Y., Bai, Y., Zhang, W., Mei, T.: Destruction and construction learning for fine-grained image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5157–5166 (2019)
Cui, Y., Zhou, F., Wang, J., Liu, X., Lin, Y., Belongie, S.: Kernel pooling for convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2930 (2017)
Ding, Y., Zhou, Y., Zhu, Y., Ye, Q., Jiao, J.: Selective sparse sampling for fine-grained image recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 6599–6608 (2019)
Dubey, A., Gupta, O., Guo, P., Raskar, R., Farrell, R., Naik, N.: Pairwise confusion for fine-grained visual classification. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 70–86 (2018)
Gao, Y., Han, X., Wang, X., Huang, W., Scott, M.: Channel interaction networks for fine-grained image categorization. In: AAAI, pp. 10818–10825 (2020)
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738 (2020)
Khosla, A., Jayadevaprakash, N., Yao, B., Li, F.F.: Novel dataset for fine-grained image categorization: stanford dogs. In: Proceedings of the CVPR Workshop on Fine-Grained Visual Categorization (FGVC), vol. 2 (2011)
Lin, D., Shen, X., Lu, C., Jia, J.: Deep lac: Deep localization, alignment and classification for fine-grained recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1666–1674 (2015)
Lin, T.Y., RoyChowdhury, A., Maji, S.: Bilinear CNN models for fine-grained visual recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1449–1457 (2015)
Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: 2008 Sixth Indian Conference on Computer Vision, Graphics and Image Processing, pp. 722–729. IEEE (2008)
Parkhi, O.M., Vedaldi, A., Zisserman, A., Jawahar, C.: Cats and dogs. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3498–3505. IEEE (2012)
Tian, Y., Krishnan, D., Isola, P.: Contrastive multiview coding. arXiv preprint arXiv:1906.05849 (2019)
Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-ucsd birds-200-2011 dataset (2011)
Wang, Z., Wang, S., Zhang, P., Li, H., Zhong, W., Li, J.: Weakly supervised fine-grained image classification via correlation-guided discriminative learning. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 1851–1860 (2019)
Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018)
Yang, Z., Luo, T., Wang, D., Hu, Z., Gao, J., Wang, L.: Learning to navigate for fine-grained classification. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 420–435 (2018)
Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., Lipson, H.: Understanding neural networks through deep visualization. arXiv preprint arXiv:1506.06579 (2015)
Yu, C., Zhao, X., Zheng, Q., Zhang, P., You, X.: Hierarchical bilinear pooling for fine-grained visual recognition. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 574–589 (2018)
Zhang, N., Donahue, J., Girshick, R., Darrell, T.: Part-based R-CNNs for fine-grained category detection. In: European Conference on Computer Vision, pp. 834–849. Springer (2014)
Zhuang, P., Wang, Y., Qiao, Y.: Learning attentive pairwise interaction for fine-grained classification. In: AAAI, pp. 13130–13137 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, F., Liu, Z., Liu, Z. (2021). Attentive Contrast Learning Network for Fine-Grained Classification. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13019. Springer, Cham. https://doi.org/10.1007/978-3-030-88004-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-88004-0_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88003-3
Online ISBN: 978-3-030-88004-0
eBook Packages: Computer ScienceComputer Science (R0)