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Learning Multi-attention Convolutional Neural Network for Fine-Grained Image Recognition
Abstract
Recognizing fine-grained categories (e.g., bird species) highly relies on discriminative part localization and part-based fine-grained feature learning. Existing approaches predominantly solve these challenges independently, while neglecting the fact that part localization (e.g., head of a bird) and fine-grained feature learning (e.g., head shape) are mutually correlated. In this paper, we propose a novel part learning approach by a multi-attention convolutional neural network (MA-CNN), where part generation and feature learning can reinforce each other. MA-CNN consists of convolution, channel grouping and part classification sub-networks. The channel grouping network takes as input feature channels from convolutional layers, and generates multiple parts by clustering, weighting and pooling from spatially-correlated channels. The part classification network further classifies an image by each individual part, through which more discriminative fine-grained features can be learned. Two losses are proposed to guide the multi-task learning of channel grouping and part classification, which encourages MA-CNN to generate more discriminative parts from feature channels and learn better fine-grained features from parts in a mutual reinforced way. MA-CNN does not need bounding box/part annotation and can be trained end-to-end. We incorporate the learned parts from MA-CNN with part-CNN for recognition, and show the best performances on three challenging published fine-grained datasets, e.g., CUB-Birds, FGVC-Aircraft and Stanford-Cars.
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Learning Deep Features for Discriminative Localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.319 · Cited by 11,428
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Caffe: Convolutional Architecture for Fast Feature EmbeddingPreprint
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5093 · Cited by 5,299
Jaderberg, M., Simonyan, K., Zisserman, A., & Kavukcuoglu, K. (2015) · arXiv · doi:10.48550/arxiv.1506.02025 · Cited by 5,137
3D Object Representations for Fine-Grained Categorization
Krause, J., Stark, M. M., Deng, J., & Fei-Fei, L. (2013) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2013.77 · Cited by 3,666
Bilinear CNN Models for Fine-Grained Visual Recognition
Lin, T., RoyChowdhury, A., & Maji, S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.170 · Cited by 2,234
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Research works printed but not linked: 2
- welinder, 2010, Caltech-Ucsd Birds 200, Technical Report
- khosla, 2011, Novel dataset for fine-grained image categorization, ICCV Workshop
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Ke, X., Huang, Y., & Guo, W. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103408 · Cited by 9
He, M., Cheng, Q., & Qi, G. (2022) · IEEE Access · doi:10.1109/access.2022.3163302 · Cited by 9
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Yuan, L., Lai, J., Zhao, J., Sun, T., Hu, C., Ye, L., Yu, G., & Yang, Z. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.896884 · Cited by 9
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Khandelwal, S., & Sigal, L. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00352 · Cited by 9
Lin, Z., Jia, J., Gao, W., & Huang, F. (2019) · IEEE Access · doi:10.1109/access.2019.2938537 · Cited by 9
Geometry-Constrained Car Recognition Using a 3D Perspective Network
Zeng, R., Zongyuan, G., Simon, D., Sridharan, S., & Fookes, C. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5468 · Cited by 10
Fruit classification using deep feature maps in the presence of deceptive similar classes
Dandekar, M., Punn, N. S., Sonbhadra, S. K., Agarwal, S., & Kiran, R. U. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533678 · Cited by 10
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Wu, X., Chang, J., Lai, Y., Yang, J., & Tian, Q. C. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3094744 · Cited by 9
Deep Attentional Structured Representation Learning for Visual RecognitionPreprint
Nakka, K. K., & Salzmann, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.05389 · Cited by 9
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Li, K., Liu, Z., & Zhang, Z. (2024) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2024.3480052 · Cited by 8
García-Pérez, A., Gómez-Silva, M. J., & de la Escalera, A. (2024) · NDT & E International · doi:10.1016/j.ndteint.2024.103303 · Cited by 8
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Liu, Y., Zhang, H., Che, X., Zhang, W., & Lu, G. (2025) · IET Image Processing · doi:10.1049/ipr2.70243 · Cited by 8
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Lin, Z., Jia, J., Gao, W., & Huang, F. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-190747 · Cited by 8
Ullah, A., Wang, J., Anwar, M. S., Ahmad, U., Saeed, U., & Fei, Z. (2019) · Electronics · doi:10.3390/electronics8121487 · Cited by 8
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Ju, M., Ryu, H., Moon, S.-K., & Yoo, C. D. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9190875 · Cited by 8
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Wang, Z., Gou, Y., Li, J., Zhang, Y., & Yang, Y. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482471 · Cited by 8
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Wang, S., Wang, Z., Wang, N., Wang, H., & Li, H. (2022) · Multimedia Systems · doi:10.1007/s00530-022-00899-6 · Cited by 8
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Chen, P., Li, P., Li, Q., & Zhang, D. (2019) · IEEE Access · doi:10.1109/access.2019.2934476 · Cited by 8
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Global Structure Graph Guided Fine-Grained Vehicle Recognition
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Figueroa-Flores, C., Raducanu, B., Berga, D., & van de Weijer, J. (2021) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0010299501630171 · Cited by 6
Metadata
- Authors
- Zheng, H., Fu, J., Mei, T., & Luo, J.
- Affiliations
- University of Science and Technology of China (CN)
- Microsoft Research Asia (China) (CN)
- University of Rochester (US)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 5219-5227
- OpenAlex
- W2773003563
- Cited by
- 1,045
- References
- 40
- Citations by year
- 2026: 33 · 2025: 71 · 2024: 126 · 2023: 153 · 2022: 143 · 2021: 206 · 2020: 145 · 2019: 111 · 2018: 56 · 2017: 1
- FWCI
- 25.02
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- fine-grained image classification, part localization, channel grouping, part classification, multi-task learning, representation learning, end-to-end learning, bird identification, aircraft recognition, vehicle identification
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