Research map / Label-efficient and robust learning / Domain adaptation and generalization
Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use RIPU region selection instead of LabOR at the 40-pixel budget | DeepLab-v2 for GTAV → Cityscapes · GTAV → Cityscapes | mIoU: 63.5 -> 65.5 | Helped |
| Use RIPU region selection instead of LabOR at the 2.2% region budget | DeepLab-v2 for GTAV → Cityscapes · GTAV → Cityscapes | mIoU: 66.6 -> 69.6 | Helped |
| Use impurity-based regions rather than random 2.2% region selection | DeepLab-v2 for GTAV → Cityscapes · GTAV → Cityscapes | mIoU: 63.8 -> 68.5 | Helped |
| Combine region impurity with prediction uncertainty rather than impurity alone (ablation) | RIPU selection on GTAV → Cityscapes · GTAV → Cityscapes | mIoU: 68.1 -> 68.5 | Helped |
| Combine region impurity with prediction uncertainty rather than uncertainty alone (ablation) | RIPU selection on GTAV → Cityscapes · GTAV → Cityscapes | mIoU: 66.2 -> 68.5 | Helped |
| Add source-domain local prediction consistency loss (ablation) | RIPU on GTAV → Cityscapes · GTAV → Cityscapes | mIoU: 68.5 -> 69.0 | Helped |
| Add target-domain negative learning loss (ablation) | RIPU on GTAV → Cityscapes · GTAV → Cityscapes | mIoU: 68.5 -> 69.2 | Helped |
| Add both consistency and negative learning losses (ablation) | RIPU on GTAV → Cityscapes · GTAV → Cityscapes | mIoU: 69.2 -> 69.6 | Helped |
| Replace adaptive regions with fixed rectangles (ablation) | RIPU on GTAV → Cityscapes · GTAV → Cityscapes | mIoU: 69.6 -> 67.7 | Hurt |
| Replace adaptive regions with superpixels (ablation) | RIPU on GTAV → Cityscapes · GTAV → Cityscapes | mIoU: 69.6 -> 66.8 | Hurt |
| Set negative threshold τ to 0.20 instead of the default 0.05 (ablation) | RIPU on GTAV → Cityscapes · GTAV → Cityscapes | mIoU: 69.62 -> 68.13 | Hurt |
| Use an appropriate threshold up to 0.10 instead of 0.20 (ablation) | RIPU on GTAV → Cityscapes · GTAV → Cityscapes | mIoU: 69.62; 68.81; 69.54; 69.17; 68.94; 68.13 | Mixed |
Closest papers by meaning
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- Balanced Learning for Domain Adaptive Semantic Segmentation (ICML 2025 · similarity 0.87)
- SemiDAViL: Semi-supervised Domain Adaptation with Vision-Language Guidance for Semantic Segmentation (CVPR 2025 · similarity 0.87)
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