Name
Abstract | ||
|---|---|---|
This paper considers zero-shot localization problem where the images used for localization are taken from new locations that are not included in the training dataset. We propose the Semantic-Assisted Location Network (SLN), which considers a new location essentially as a new combination of certain semantic classes. Moreover, we propose an iterative zero-shot learning method based on Expectation-Maximization (EM) algorithm to deal with the problem that the inter-class relationships of class representations in image embedding space and class embedding space are inconsistent. Experiments show that the proposed iterative zero-shot learning method outperforms start-of-the-art zero-shot localization methods by a large margin. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LRA.2022.3153715 | IEEE Robotics and Automation Letters |
Keywords | DocType | Volume |
Localization,Recognition,Transfer Learning | Journal | 7 |
Issue | ISSN | Citations |
3 | 2377-3766 | 0 |
PageRank | References | Authors |
0.34 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yukun Yang | 1 | 0 | 0.34 |
| Liang Zhao | 2 | 100 | 13.74 |
| Xiangdong Liu | 3 | 11 | 5.96 |