Name
Abstract | ||
|---|---|---|
Semantic patterns offine-grained objects are determined by subtle appearance difference of local parts, which thus inspires a number of part-based methods. However, due to uncontrollable object poses in images, distinctive de-tails carried by local regions can be spatially distributed or even self-occluded, leading to a large variation on ob-ject representation. For discounting pose variations, this paper proposes to learn a novel graph based object rep-resentation to reveal a global configuration of local parts for self-supervised pose alignment across classes, which is employed as an auxiliary feature regularization on a deep representation learning network. Moreover, a coarse-to-fine supervision together with the proposed pose-insensitive constraint on shallow-to-deep sub-networks encourages discriminative features in a curriculum learning manner. We evaluate our method on three popular fine-grained ob-ject classification benchmarks, consistently achieving the state-of-the-art performance. Source codes are available at https://github.com/yangxhll/P2P-Net. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CVPR52688.2022.00725 | IEEE Conference on Computer Vision and Pattern Recognition |
Keywords | DocType | Volume |
Recognition: detection,categorization,retrieval, Representation learning, Self-& semi-& meta- & unsupervised learning | Conference | 2022 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuhui Yang | 1 | 0 | 0.34 |
| Yaowei Wang | 2 | 134 | 29.62 |
| Ke Chen | 3 | 0 | 0.34 |
| Yong Xu | 4 | 0 | 0.34 |
| Yonghong Tian | 5 | 1057 | 102.81 |