Research map / Label-efficient and robust learning / Domain adaptation and generalization
Unsupervised Domain Adaptation for Semantic Segmentation by Content Transfer
Read the paperCheck 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 |
|---|---|---|---|
| Use the proposed method instead of BDL | Semantic segmentation with DeepLab v2 and ResNet-101 · GTA5 → Cityscapes val | mIoU: 48.5 -> 49.6 | Helped |
| Use the proposed method instead of APODA | Semantic segmentation with DeepLab v2 and ResNet-101 · SYNTHIA → Cityscapes val | mIoU*: 53.1 -> 53.9 | Helped |
| Add zero-style loss (ablation) | DISE · GTA5 → Cityscapes | mIoU: 45.4 -> 46.6 | Helped |
| Add zero-style loss (ablation) | DISE · SYNTHIA → Cityscapes | mIoU: 41.5 -> 43.3 | Helped |
| Add cycle-consistency learning (ablation) | Zero-style loss variant · GTA5 → Cityscapes | mIoU: 46.6 -> 46.9 | Helped |
| Add cycle-consistency learning (ablation) | Zero-style loss variant · SYNTHIA → Cityscapes | mIoU: 43.3 -> 43.5 | Helped |
| Add self-training with target pseudo labels (ablation) | Zero-style loss + cycle-consistency variant · GTA5 → Cityscapes | mIoU: 46.9 -> 48.0 | Helped |
| Add self-training with target pseudo labels (ablation) | Zero-style loss + cycle-consistency variant · GTA5 → Cityscapes | mIoU tail: 30.6 -> 29.9 | Hurt |
| Add self-training with target pseudo labels (ablation) | Zero-style loss + cycle-consistency variant · SYNTHIA → Cityscapes | mIoU: 43.5 -> 43.9 | Helped |
| Add self-training with target pseudo labels (ablation) | Zero-style loss + cycle-consistency variant · SYNTHIA → Cityscapes | mIoU tail: 24.0 -> 23.2 | Hurt |
| Add input-level content transfer (ablation) | Self-training variant · GTA5 → Cityscapes | mIoU: 48.0 -> 48.7 | Helped |
| Add input-level content transfer (ablation) | Self-training variant · SYNTHIA → Cityscapes | mIoU tail: 23.2 -> 26.3 | Helped |
Closest papers by meaning
- Universal Domain Adaptation for Semantic Segmentation (CVPR 2025 · similarity 0.94)
- Unsupervised Model Adaptation for Continual Semantic Segmentation (AAAI 2021 · similarity 0.92)
- Balanced Learning for Domain Adaptive Semantic Segmentation (ICML 2025 · similarity 0.92)
- Source-Free Domain Adaptation for Semantic Segmentation (CVPR 2021 · similarity 0.91)
- Towards Unsupervised Domain Bridging via Image Degradation in Semantic Segmentation (NeurIPS 2025 · similarity 0.90)
- Open-Set Domain Adaptation for Semantic Segmentation (CVPR 2024 · 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.