Name
Abstract | ||
|---|---|---|
Unsupervised domain adaptation algorithms aim to transfer the knowledge learned from one domain to another (e.g., synthetic to real images). The adapted representations often do not capture pixel-level domain shifts that are crucial for dense prediction tasks (e.g., semantic segmentation). In this met; we present a novel pixel-wise adversarial domain adaptation algorithm. By leveraging image-to-image translation methods for data augmentation, our key insight is that while the translated images between domains may differ in styles, their predictions for the task should be consistent. We exploit this property and introduce a cross-domain consistency loss that enforces our adapted model to produce consistent predictions. Through extensive experimental results, we show that our method compares favorably against the state-of-the-art on a wide variety of unsupervised domain adaptation tasks. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CVPR.2019.00189 | 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) |
Field | DocType | ISSN |
Computer vision,Pattern recognition,Computer science,Artificial intelligence,Pixel | Conference | 1063-6919 |
Citations | PageRank | References |
13 | 0.50 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yun-Chun Chen | 1 | 16 | 2.57 |
| Yen-Yu Lin | 2 | 463 | 39.75 |
| Yang Ming-Hsuan | 3 | 15303 | 620.69 |
| Jia-Bin Huang | 4 | 920 | 42.90 |