% Key papers for Adaptation for semantic segmentation (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2018_learning_13316,
  title = {{Learning to Adapt Structured Output Space for Semantic Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Tsai_Learning_to_Adapt_CVPR_2018_paper.html},
}

@inproceedings{cvpr2020_fda_15516,
  title = {{FDA: Fourier Domain Adaptation for Semantic Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2019_taking_14895,
  title = {{Taking a Closer Look at Domain Shift: Category-Level Adversaries for Semantics Consistent Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2019_bidirectional_13894,
  title = {{Bidirectional Learning for Domain Adaptation of Semantic Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{icml2018_cycada_36607,
  title = {{CyCADA: Cycle-Consistent Adversarial Domain Adaptation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2286},
}

@inproceedings{cvpr2022_daformer_18564,
  title = {{DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2021_prototypical_17674,
  title = {{Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2018_learning_13283,
  title = {{Learning From Synthetic Data: Addressing Domain Shift for Semantic Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Sankaranarayanan_Learning_From_Synthetic_CVPR_2018_paper.html},
}

@inproceedings{cvpr2020_unsupervised_16418,
  title = {{Unsupervised Intra-Domain Adaptation for Semantic Segmentation Through Self-Supervision}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2023_mic_21489,
  title = {{MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21899},
}

@inproceedings{cvpr2019_crdoco_13979,
  title = {{CrDoCo: Pixel-Level Domain Transfer With Cross-Domain Consistency}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2020_learning_15784,
  title = {{Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2021_source_17918,
  title = {{Source-Free Domain Adaptation for Semantic Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2019_all_13839,
  title = {{All About Structure: Adapting Structural Information Across Domains for Boosting Semantic Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2020_differential_15409,
  title = {{Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2020_xmuda_16511,
  title = {{xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2022_semantic_19847,
  title = {{Semantic-Aware Domain Generalized Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2021_pixmatch_17607,
  title = {{PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2020_phase_16032,
  title = {{Phase Consistent Ecological Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2020_open_15991,
  title = {{Open Compound Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2024_open_24366,
  title = {{Open-Set Domain Adaptation for Semantic Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31647},
}

@inproceedings{cvpr2024_stable_24885,
  title = {{Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31277},
}

@inproceedings{cvpr2024_semantics_24801,
  title = {{Semantics Distortion and Style Matter: Towards Source-free UDA for Panoramic Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30544},
}

@inproceedings{cvpr2025_universal_28016,
  title = {{Universal Domain Adaptation for Semantic Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34536},
}

@inproceedings{cvpr2025_semidavil_27639,
  title = {{SemiDAViL: Semi-supervised Domain Adaptation with Vision-Language Guidance for Semantic Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/35137},
}

@inproceedings{cvpr2025_semantic_27633,
  title = {{Semantic Library Adaptation: LoRA Retrieval and Fusion for Open-Vocabulary Semantic Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/32620},
}

@inproceedings{aaai2024_density_5667,
  title = {{Density Matters: Improved Core-Set for Active Domain Adaptive Segmentation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29308},
}

@inproceedings{neurips2024_unidseg_73073,
  title = {{UniDSeg: Unified Cross-Domain 3D Semantic Segmentation via Visual Foundation Models Prior}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94354},
}

@inproceedings{icml2025_masktwins_46318,
  title = {{MaskTwins: Dual-form Complementary Masking for Domain-Adaptive Image Segmentation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46243},
}

@inproceedings{neurips2024_eagle_70954,
  title = {{EAGLE: Efficient Adaptive Geometry-based Learning in Cross-view Understanding}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96249},
}

@inproceedings{aaai2025_omni_10159,
  title = {{Omni-Query Active Learning for Source-Free Domain Adaptive Cross-Modality 3D Semantic Segmentation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32941},
}

@inproceedings{icml2025_balanced_46314,
  title = {{Balanced Learning for Domain Adaptive Semantic Segmentation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46247},
}

@inproceedings{neurips2025_towards_78312,
  title = {{Towards Unsupervised Domain Bridging via Image Degradation in Semantic Segmentation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=dFlqhts0jS},
}

@inproceedings{iclr2025_dynalign_81616,
  title = {{DynAlign: Unsupervised Dynamic Taxonomy Alignment for Cross-Domain Segmentation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30161},
}

@inproceedings{cvpr2026_panda_95827,
  title = {{PanDA: Unsupervised Domain Adaptation for Multimodal 3D Panoptic Segmentation in Autonomous Driving}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Pan_PanDA_Unsupervised_Domain_Adaptation_for_Multimodal_3D_Panoptic_Segmentation_in_CVPR_2026_paper.html},
}

@inproceedings{cvpr2026_denoise_98134,
  title = {{Denoise and Align: Towards Source-Free UDA for Robust Panoramic Semantic Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Chang_Denoise_and_Align_Towards_Source-Free_UDA_for_Robust_Panoramic_Semantic_CVPR_2026_paper.html},
}

@inproceedings{cvpr2026_masked_98212,
  title = {{Masked Representation Modeling for Domain-Adaptive Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Zhou_Masked_Representation_Modeling_for_Domain-Adaptive_Segmentation_CVPR_2026_paper.html},
}