Name
Title | ||
|---|---|---|
Attention-based cropping and erasing learning with coarse-to-fine refinement for fine-grained visual classification |
Abstract | ||
|---|---|---|
•Attention regions cropping and erasing data augmentation approaches are proposed for fine-grained visual classification.•A coarse-to-fine refinement strategy is proposed to refine the classification result with the defined confidence value.•Analyses of three challenging fine-grained datasets along with currently outstanding methods.•The comprehensive experimental results on three challenging FGVC datasets show the effectiveness of our approach. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.neucom.2022.06.041 | Neurocomputing |
Keywords | DocType | Volume |
Fine-grained visual classification,Attention-based data augmentation,Coarse-to-fine refinement | Journal | 501 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianpin Chen | 1 | 0 | 0.34 |
| Heng Li | 2 | 0 | 0.34 |
| Junlin Liang | 3 | 0 | 0.34 |
| Xiaofan Su | 4 | 0 | 0.34 |
| Zhenzhen Zhai | 5 | 0 | 0.34 |
| Xinyu Chai | 6 | 5 | 1.76 |