Research map / Label-efficient and robust learning / Domain adaptation and generalization
Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation
Check your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add output-space adversarial training | Source-only segmentation model · GTA5 → Cityscapes, Cityscapes val | mIoU: 36.6 -> 41.4 | Helped |
| Use CycleGAN-transferred GTA5 images with adversarial adaptation | GTA5 → Cityscapes model · GTA5 → Cityscapes, Cityscapes val | mIoU: 41.4 -> 44.9 | Helped |
| Add stuff and instance matching (SIM) (ablation) | AA + IT · GTA5 → Cityscapes, Cityscapes val | mIoU: 44.9 -> 46.2 | Helped |
| Add self-supervised retraining using confidence-filtered pseudo-labels (ablation) | SIM · GTA5 → Cityscapes, Cityscapes val | mIoU: 46.2 -> 49.2 | Helped |
| Add adversarial training and use transferred source-domain images | Source-only segmentation model · SYNTHIA → Cityscapes, Cityscapes val (13 classes) | mIoU: 38.6 -> 46.0 | Helped |
| Add stuff and instance matching (SIM) (ablation) | Adversarial adaptation with transferred images · SYNTHIA → Cityscapes, Cityscapes val (13 classes) | mIoU: 46.0 -> 47.1 | Helped |
| Add self-supervised retraining using pseudo-labels (ablation) | SIM · SYNTHIA → Cityscapes, Cityscapes val (13 classes) | mIoU: 47.1 -> 52.1 | Helped |
Closest papers by meaning
- Taking a Closer Look at Domain Shift: Category-Level Adversaries for Semantics Consistent Domain Adaptation (CVPR 2019 · similarity 0.92)
- Universal Domain Adaptation for Semantic Segmentation (CVPR 2025 · similarity 0.91)
- Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation (NeurIPS 2019 · similarity 0.91)
- Coarse-To-Fine Domain Adaptive Semantic Segmentation With Photometric Alignment and Category-Center Regularization (CVPR 2021 · similarity 0.90)
- Open-Set Domain Adaptation for Semantic Segmentation (CVPR 2024 · similarity 0.90)
- Balanced Learning for Domain Adaptive Semantic Segmentation (ICML 2025 · similarity 0.90)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.