Name
Abstract | ||
|---|---|---|
Traditional place categorization approaches in robot vision assume that training and test images have similar visual appearance. Therefore, any seasonal, illumination, and environmental changes typically lead to severe degradation in performance. To cope with this problem, recent works have been proposed to adopt domain adaptation techniques. While effective, these methods assume that some prior i... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LRA.2018.2809700 | IEEE Robotics and Automation Letters |
Keywords | DocType | Volume |
Robots,Training,Data models,Computational modeling,Adaptation models,Visualization,Semantics | Journal | 3 |
Issue | Citations | PageRank |
3 | 7 | 0.47 |
References | Authors | |
24 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Massimiliano Mancini | 1 | 24 | 8.86 |
| Samuel Rota Bulò | 2 | 564 | 33.69 |
| Barbara Caputo | 3 | 3298 | 201.26 |
| Elisa Ricci 0002 | 4 | 1393 | 73.75 |