Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation
The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models. Unsupervised domain adaptation (UDA) attempts to solve such problem. Recent works show that self-training is a powerful approach to UDA. However, existing methods have...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - PP(2025) vom: 18. März
|
|---|
| 1. Verfasser: |
Zhu, Chuang
(VerfasserIn) |
|---|
| Weitere Verfasser: |
Liu, Kebin,
Tang, Wenqi,
Mei, Ke,
Zou, Jiaqi,
Huang, Tiejun |
|---|
| Format: | Online-Aufsatz
|
|---|
| Sprache: | English |
|---|
| Veröffentlicht: |
2025
|
|---|
| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence
|
|---|
| Schlagworte: | Journal Article |
|---|