The DOI matches its Crossref record; 37 of the 40 references it cites were found in a registry or an index.
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
Fu, J., Zheng, H., & Mei, T. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.476 · Cited by 1,469
Self-citationFine-Grained Visual Classification of AircraftPreprint
Maji, S., Rahtu, E., Kannala, J., Blaschko, M. B., & Vedaldi, A. (2013) · HAL · doi:10.48550/arxiv.1306.5151 · Cited by 1,250
Part-Based R-CNNs for Fine-Grained Category Detection
Zhang, N., Donahue, J., Girshick, R., & Darrell, T. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10590-1_54 · Cited by 1,225
A large-scale car dataset for fine-grained categorization and verification
Yang, L., Luo, P., Loy, C. C., & Tang, X. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299023 · Cited by 879
A Visual Vocabulary for Flower Classification
Nilsback, M.-E., & Zisserman, A. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.42 · Cited by 876
Learning Deep Representations of Fine-Grained Visual Descriptions
Reed, S., Akata, Z., Lee, H., & Schiele, B. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.13 · Cited by 818
Xiao, T., Xu, Y., Yang, K., Zhang, J., Peng, Y., & Zhang, Z. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298685 · Cited by 807
Bird Species Categorization Using Pose Normalized Deep Convolutional NetsPreprint
Branson, S., Horn, G. V., Belongie, S., & Perona, P. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.2952 · Cited by 600
Part-Stacked CNN for Fine-Grained Visual Categorization
Huang, S., Xu, Z., Tao, D., & Zhang, Y. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.132 · Cited by 482
Fine-grained recognition without part annotations
Krause, J., Jin, H., Yang, J., & Fei-Fei, L. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299194 · Cited by 472
Diversified Visual Attention Networks for Fine-Grained Object Classification
Zhao, B., Wu, X., Feng, J., Peng, Q., & Yan, S. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2648498 · Cited by 428
Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks
Simon, M., & Rodner, E. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.136 · Cited by 400
Deep LAC: Deep localization, alignment and classification for fine-grained recognition
Lin, D., Shen, X., Lu, C., & Jia, J. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298775 · Cited by 354
Picking Deep Filter Responses for Fine-Grained Image Recognition
Zhang, X., Xiong, H., Zhou, W., Lin, W., & Tian, Q. C. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.128 · Cited by 332
SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-Grained Recognition
Zhang, H., Xu, T., Elhoseiny, M., Huang, X., Zhang, S., Elgammal, A., & Metaxas, D. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.129 · Cited by 301
Multiple Granularity Descriptors for Fine-Grained Categorization
Wang, D., Shen, Z., Shao, J., Zhang, W., Xue, X., & Zhang, Z. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.276 · Cited by 231
Learning Deep Representations of Fine-grained Visual DescriptionsPreprint
Reed, S., Akata, Z., Schiele, B., & Lee, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.05395 · Cited by 156
Fisher vectors meet Neural Networks: A hybrid classification architecture
Perronnin, F., & Larlus, D. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298998 · Cited by 141
Boosted Convolutional Neural Networks
Moghimi, M. K., Belongie, S., Saberian, M., Yang, J. W., Vasconcelos, N. M., & Li, L.-J. (2016) · British Machine Vision Conference (BMVC) · doi:10.5244/c.30.24 · Cited by 136
Mining Discriminative Triplets of Patches for Fine-Grained Classification
Wang, Y., Choi, J., Morariu, V. I., & Davis, L. S. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.131 · Cited by 129
Revisiting the Fisher vector for fine-grained classification
Gosselin, P.-H., Murray, N., Jeǵou, H., & Perronnin, F. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.06.011 · Cited by 119
Parkhi, O., Vedaldi, A., Jawahar, C. V., & Zisserman, A. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126398 · Cited by 113
Mask-CNN: Localizing Parts and Selecting Descriptors for Fine-Grained Image RecognitionPreprint
Wei, X.-S., Xie, C.-W., & Wu, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06878 · Cited by 103
Liu, X., Xia, T., Wang, J., & Lin, Y. (2016) · arXiv (Cornell University) · Cited by 78
Relaxing from Vocabulary: Robust Weakly-Supervised Deep Learning for Vocabulary-Free Image Tagging
Fu, J., Wu, Y., Mei, T., Wang, J., Lu, H., & Rui, Y. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.230 · Cited by 38
Self-citationTagging Personal Photos with Transfer Deep Learning
Fu, J., Mei, T., Yang, K., Lu, H., & Rui, Y. (2015) · International World Wide Web Conference (WWW) · doi:10.1145/2736277.2741112 · Cited by 36
Self-citationImage Tag Refinement With View-Dependent Concept Representations
Fu, J., Wang, J., Rui, Y., Wang, X., Mei, T., & Lu, H. (2014) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2014.2380211 · Cited by 30
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3 of the 40 references couldn’t be matched to a known paper.
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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Attention mechanisms in computer vision: A survey
Guo, M.-H., Xu, T.-X., Liu, J.-J., Liu, Z.-N., Jiang, P.-T., Mu, T.-J., Zhang, S.-H., Martin, R. R., Cheng, M.-M., & Hu, S.-M. (2022) · Computational Visual Media · doi:10.1007/s41095-022-0271-y · Cited by 2,566
“[8], [9], [15], [21], [31], [32], [34], [35] , [22], [26], [62], [63], [64], [65], [66], [67] , [41], [68], [69], [70], [71], [72], [73], [74] , [8], [34], [42], [43], [75], [76], [77], [78] , [27], [44], [45], [46], [79], [80], [81], [82] , [61], [83], [84], [85], [86], [87], [88], [89] , [47], [90], [91], [92], [93]…”
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Zhao, H., Wei, T., Zhou, W., Zhang, W., Chen, D., & Yu, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00222 · Cited by 1,019
“Fine-grained classification [50, 49, 13, 37, 12, 52, 47, 17, 10] is a challenging research task in computer vision, which captures the local discriminative features to distinguish different fine-grained categories.”
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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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