Research map / Label-efficient and robust learning / Semi-supervised learning
Semi-Supervised Semantic Segmentation With Error Localization Network
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add L_contra alongside L_pseudo (ablation) | ELN · PASCAL VOC 2012, 1/20 | mIoU: 69.14 -> 70.52 | Helped |
| Add L_pseudo alongside L_contra (ablation) | ELN · PASCAL VOC 2012, 1/20 | mIoU: 69.30 -> 70.52 | Helped |
| Use the combined L_pseudo + L_contra losses instead of thresholding without an additional network | ELN · PASCAL VOC 2012, 1/20 | mIoU: 67.77 -> 70.52 | Helped |
| Add an entropy map (ablation) | ELN · PASCAL VOC 2012, 1/4 | mIoU: 73.6 -> 74.6 | Helped |
Closest papers by meaning
- GuidedMix-Net: Semi-supervised Semantic Segmentation by Using Labeled Images as Reference (AAAI 2022 · similarity 0.90)
- PseudoSeg: Designing Pseudo Labels for Semantic Segmentation (ICLR 2021 · similarity 0.90)
- Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant (NeurIPS 2022 · similarity 0.90)
- Semi-Supervised Semantic Segmentation With Cross Pseudo Supervision (CVPR 2021 · similarity 0.90)
- RWMS: Reliable Weighted Multi-Phase for Semi-supervised Segmentation (AAAI 2024 · similarity 0.88)
- W2P: Switching from Weak Supervision to Partial Supervision for Semantic Segmentation (AAAI 2024 · similarity 0.87)
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