Research map / Label-efficient and robust learning / Semi-supervised learning
DAW: Exploring the Better Weighting Function for Semi-supervised Semantic Segmentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace fixed threshold with distribution-aware weighting function (ablation) | UniMatch baseline · PASCAL classic 92, ResNet-50 | mIoU: 66.9 -> 68.0 | Helped |
| add distribution alignment on top of DAWF (ablation) | DAW · PASCAL classic 92, ResNet-50 | mIoU: 68.0 -> 68.5 | Helped |
| use fixed threshold instead of no threshold (ablation) | Pseudo-Label · PASCAL classic 92, ResNet-50 | mIoU: 62.7 -> 66.9 | Helped |
| increase EMA momentum from 0.99 to 0.9999 (ablation) | DAW · PASCAL classic 92, ResNet-50 | mIoU: 68.5 -> 67.9 | Hurt |
| DAW vs best prior SSL method on 1/16 labels | DAW · PASCAL classic 92, ResNet-101 | mIoU: 74.8 vs 71.1 (AugSeg) | Helped |
| DAW vs AugSeg on Pascal classic 1/8, ResNet-50 | DAW · PASCAL classic 183, ResNet-50 | mIoU: 72.2 -> 73.1 | Helped |
| DAW vs CPS/CCT on electron microscopy | DAW · Lucchi 1/32 | mIoU: 84.7 -> 85.9 | Helped |
Closest papers by meaning
- Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels (CVPR 2022 · similarity 0.91)
- RWMS: Reliable Weighted Multi-Phase for Semi-supervised Segmentation (AAAI 2024 · similarity 0.89)
- Improving Semi-Supervised Semantic Segmentation with Sliced-Wasserstein Feature Alignment and Uniformity (CVPR 2025 · similarity 0.89)
- SemiDAViL: Semi-supervised Domain Adaptation with Vision-Language Guidance for Semantic Segmentation (CVPR 2025 · similarity 0.89)
- Beyond Confidence: Exploiting Homogeneous Pattern for Semi-Supervised Semantic Segmentation (ICML 2025 · similarity 0.88)
- Label-Efficient Few-Shot Semantic Segmentation with Unsupervised Meta-Training (AAAI 2024 · similarity 0.88)
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