The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification From the Bottom Up
Open accessAbstract
Given a training dataset composed of images and corresponding category labels, deep convolutional neural networks show a strong ability in mining discriminative parts for image classification. However, deep convolutional neural networks trained with image level labels only tend to focus on the most discriminative parts while missing other object parts, which could provide complementary information. In this paper, we approach this problem from a different perspective. We build complementary parts models in a weakly supervised manner to retrieve information suppressed by dominant object parts detected by convolutional neural networks. Given image level labels only, we first extract rough object instances by performing weakly supervised object detection and instance segmentation using Mask R-CNN and CRF-based segmentation. Then we estimate and search for the best parts model for each object instance under the principle of preserving as much diversity as possible. In the last stage, we build a bi-directional long short-term memory (LSTM) network to fuze and encode the partial information of these complementary parts into a comprehensive feature for image classification. Experimental results indicate that the proposed method not only achieves significant improvement over our baseline models, but also outperforms state-of-the-art algorithms by a large margin (6.7%, 2.8%, 5.2% respectively) on Stanford Dogs 120, Caltech-UCSD Birds 2011-200 and Caltech 256.
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Fine-Grained Image Analysis With Deep Learning: A Survey
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The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
Chang, D., Ding, Y., Xie, J., Bhunia, A. K., Li, X., Ma, Z., Wu, M., Guo, J., & Song, Y.-Z. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2973812 · Cited by 401
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification
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Metadata
- Authors
- Ge, W., Lin, X., & Yu, Y.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 3029-3038
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963393555
- Cited by
- 317
- References
- 50
- FWCI
- 14.25
- Type
- conference paper
- Language
- en
- Open access
- green
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