Name
Abstract | ||
|---|---|---|
Object transfiguration aims to translate the domain of objects in an image. In this paper, we challenge a new task: category independent object transfiguration, which enables objects to be transfigured even for object categories not included in the training data. Conventional methods are based on the premise that the object categories in the test images are contained in the training images. Therefore, they can train transfer regions and magnitude implicitly, and they accurately estimate the transfer regions and magnitude in test images. However, when an image containing object categories not included in the training data is input, this premise breaks down. Consequently, undesired regions are converted with undesired magnitude. To tackle this problem, we propose a domain region and magnitude aware GAN that explicitly predicts transfer region and magnitude, and translates so that the predicted region and magnitude before and after translation are the same. Experimental results show that our method can more realistically and accurately translate the object domains than the state-of-the-art method. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-41404-7_50 | ACPR (1) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kaori Kumagai | 1 | 0 | 0.34 |
| Yukito Watanabe | 2 | 0 | 0.34 |
| Takashi Hosono | 3 | 0 | 1.01 |
| Jun Shimamura | 4 | 0 | 0.34 |
| Atsushi Sagata | 5 | 0 | 1.01 |