Name
Abstract | ||
|---|---|---|
In this paper, we tackle the long-tailed visual recognition problem from the categorical prototype perspective by proposing a prototype-based classifier learning (PCL) method. Specifically, thanks to the generalization ability and robustness, categorical prototypes reveal their advantages of representing the category semantics. Coupled with their class-balance characteristic, categorical prototypes also show potential for handling data imbalance. In our PCL, we propose to generate the categorical classifiers based on the prototypes by performing a learnable mapping function. To further alleviate the impact of imbalance on classifier generation, two kinds of classifier calibration approaches are designed from both prototype-level and example-level aspects. Extensive experiments on five benchmark datasets, including the large-scale iNaturalist, Places-LT, and ImageNet-LT, justify that the proposed PCL can outperform state-of-the-arts. Furthermore, validation experiments can demonstrate the effectiveness of tailored designs in PCL for long-tailed problems. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s11432-021-3489-1 | Science China Information Sciences |
Keywords | DocType | Volume |
long-tailed distribution, categorical prototype, classifier generation, classifier calibration, class imbalance | Journal | 65 |
Issue | ISSN | Citations |
6 | 1674-733X | 0 |
PageRank | References | Authors |
0.34 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| xiushen wei | 1 | 0 | 0.34 |
| shulin xu | 2 | 0 | 0.34 |
| Hao Chen | 3 | 211 | 37.88 |
| Liang Xiao | 4 | 431 | 65.25 |
| Yuxin Peng | 5 | 1122 | 74.90 |