Research map / Label-efficient and robust learning / Domain adaptation and generalization
Unsupervised Model Adaptation for Continual Semantic Segmentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Adapt the source-trained model using a source prototypical distribution and unlabeled target data | MAS³ · SYNTHIA→Cityscapes | mIoU: 20.2 -> 44.7 | Helped |
| Use MAS³ instead of PCEDA | SYNTHIA→Cityscapes · SYNTHIA→Cityscapes | mIoU: 48.7 -> 44.7 | Hurt |
| Adapt the source-trained model using a source prototypical distribution and unlabeled target data | MAS³ · GTA5→Cityscapes | mIoU: 24.9 -> 43.9 | Helped |
| Use MAS³ instead of PCEDA | GTA5→Cityscapes · GTA5→Cityscapes | mIoU: 44.6 -> 43.9 | Hurt |
| Add confidence filtering when fitting the GMM rather than using all samples (ablation) | MAS³ · GTA5→Cityscapes | mIoU: 41.6 -> 43.9 | Helped |
| Use high-confidence samples rather than a portion of samples to fit the GMM (ablation) | MAS³ · GTA5→Cityscapes | mIoU: 42.7 -> 43.9 | Helped |
| Vary the trade-off parameter λ (ablation) | MAS³ · Domain adaptation benchmarks | mIoU | No clear effect |
Closest papers by meaning
- Universal Domain Adaptation for Semantic Segmentation (CVPR 2025 · similarity 0.94)
- Source-Free Domain Adaptation for Semantic Segmentation (CVPR 2021 · similarity 0.93)
- Towards Better Stability and Adaptability: Improve Online Self-Training for Model Adaptation in Semantic Segmentation (CVPR 2023 · similarity 0.93)
- Balanced Learning for Domain Adaptive Semantic Segmentation (ICML 2025 · similarity 0.92)
- Open-Set Domain Adaptation for Semantic Segmentation (CVPR 2024 · similarity 0.91)
- Towards Unsupervised Domain Bridging via Image Degradation in Semantic Segmentation (NeurIPS 2025 · similarity 0.90)
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