The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
The application of two-level attention models in deep convolutional neural network for fine-grained image classification
Open accessAbstract
Fine-grained classification is challenging because categories can only be discriminated by subtle and local differences. Variances in the pose, scale or rotation usually make the problem more difficult. Most fine-grained classification systems follow the pipeline of finding foreground object or object parts (where) to extract discriminative features (what). In this paper, we propose to apply visual attention to fine-grained classification task using deep neural network. Our pipeline integrates three types of attention: the bottom-up attention that propose candidate patches, the object-level top-down attention that selects relevant patches to a certain object, and the part-level top-down attention that localizes discriminative parts. We combine these attentions to train domain-specific deep nets, then use it to improve both the what and where aspects. Importantly, we avoid using expensive annotations like bounding box or part information from end-to-end. The weak supervision constraint makes our work easier to generalize. We have verified the effectiveness of the method on the subsets of ILSVRC2012 dataset and CUB200 2011 dataset. Our pipeline delivered significant improvements and achieved the best accuracy under the weakest supervision condition. The performance is competitive against other methods that rely on additional annotations.
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Cites
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Cited by
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al. (2019) · arXiv · doi:10.48550/arxiv.1910.10045 · Cited by 11,321
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“In the same line, [278] presented a three-level attention model to perform a fine-grained classifi-cation task.”
Recent advances in convolutional neural networks
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Residual Attention Network for Image Classification
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A review on the attention mechanism of deep learning
Niu, Z., Zhong, G., & Yu, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.091 · Cited by 3,379
Metadata
- Authors
- Xiao, T., Xu, Y., Yang, K., Zhang, J., Peng, Y., & Zhang, Z.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 842-850
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1928906481
- Cited by
- 771
- References
- 25
- FWCI
- 27.85
- Type
- conference paper
- Language
- en
- Open access
- green
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- Xiao, T., Xu, Y., Yang, K., Zhang, J., Peng, Y., & Zhang, Z. (2015). The application of two-level attention models in deep convolutional neural network for fine-grained image classification. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2015.7298685
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