Name
Abstract | ||
|---|---|---|
Fine-grained visual categorization (FGVC) is challenging mainly due to the large intra-class confusion and small inter-class variance in terms of shape, pose, and appearance. We propose the concept of fine-grained label and that any given label can be further classified into some sub-classes as fine-grained labels, and thus samples of each original label are classifed into several sub-classes in which only more familiar samples are given the same fine-grained label. The samples of fine-grained labels have less intra-class confusion and bigger inter-class variance. Besides, fine-grained labels can be obtained through unsupervised means without any domain knowledge or annotations. Instead of training on the fine-grained labels directly, we utilize these "free" labels as an auxiliary task to regularize the training of the deep learning model. In the test phase, as sub-classes of the original label, the predicted fine-grained labels are used for integration with original labels to get the final classification results. Experiments on the popular CUB-200-2011 dataset demonstrate that employing the proposed fine-grained labels in CNN model improves performance from both training and test phases.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3307363.3307382 | Proceedings of the 11th International Conference on Computer Modeling and Simulation |
Keywords | DocType | ISBN |
fine-grained label, fine-grained visual categorization, integration, regularize | Conference | 978-1-4503-6619-9 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junfeng Wu | 1 | 5 | 2.77 |
| Li Yao | 2 | 7 | 2.17 |
| Bin Liu | 3 | 10 | 3.53 |
| Zheyuan Ding | 4 | 4 | 1.75 |