Research map / Label-efficient and robust learning / Self-supervised and contrastive
Self-Supervised Augmentation Consistency for Adapting Semantic Segmentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SAC instead of the strongest reported baseline, SA-I2I | VGG-16 segmentation model · GTA5 → Cityscapes | mIoU: 46.5 -> 49.9 | Helped |
| Use SAC instead of the strongest reported baseline, RPT | ResNet-101 segmentation model · GTA5 → Cityscapes | mIoU: 52.6 -> 53.8 | Helped |
| Use SAC instead of the strongest reported baseline, SA-I2I | VGG-16 segmentation model · SYNTHIA → Cityscapes | mIoU: 41.5 -> 49.1 | Helped |
| Use SAC instead of the strongest reported baseline, RPT | ResNet-101 segmentation model · SYNTHIA → Cityscapes | mIoU: 51.2 -> 52.6 | Helped |
| Add augmentation consistency (ablation) | SAC · GTA5 → Cityscapes, VGG-16 | mIoU: 41.9 -> 49.9 | Helped |
| Add a momentum network (ablation) | SAC · GTA5 → Cityscapes, VGG-16 | mIoU: 43.5 -> 49.9 | Helped |
| Add photometric noise (ablation) | SAC · GTA5 → Cityscapes, VGG-16 | mIoU: 46.0 -> 49.9 | Helped |
| Add multi-scale fusion (ablation) | SAC · GTA5 → Cityscapes, VGG-16 | mIoU: 47.3 -> 49.9 | Helped |
| Add focal loss (ablation) | SAC · GTA5 → Cityscapes, VGG-16 | mIoU: 47.5 -> 49.9 | Helped |
| Average overlapping predictions instead of selecting minimum-entropy predictions (ablation) | SAC · GTA5 → Cityscapes, VGG-16 | mIoU: 48.0 -> 49.9 | Helped |
| Add class-based thresholding (ablation) | SAC · GTA5 → Cityscapes, VGG-16 | mIoU: 48.2 -> 49.9 | Helped |
| Add confidence regularisation (ablation) | SAC · GTA5 → Cityscapes, VGG-16 | mIoU: 48.3 -> 49.9 | Helped |
Closest papers by meaning
- Unsupervised Intra-Domain Adaptation for Semantic Segmentation Through Self-Supervision (CVPR 2020 · similarity 0.91)
- Bidirectional Learning for Domain Adaptation of Semantic Segmentation (CVPR 2019 · similarity 0.91)
- On the Road to Online Adaptation for Semantic Image Segmentation (CVPR 2022 · similarity 0.90)
- Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021 · similarity 0.90)
- Balanced Learning for Domain Adaptive Semantic Segmentation (ICML 2025 · similarity 0.90)
- Pay Attention to Target: Relation-Aware Temporal Consistency for Domain Adaptive Video Semantic Segmentation (AAAI 2024 · similarity 0.89)
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