# Domain Adaptive and Generalizable Network Architectures and Training Strategies for Semantic Image Segmentation

arXiv: https://arxiv.org/abs/2304.13615 · HTML: https://mlsanity.com/paper/2304.13615/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 72 of its 94 references that are in the map.
- First posted on arXiv 26 Apr 2023.

## Builds on (most cited first, 30 of 72)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness](https://mlsanity.com/paper/1811.12231/) (2018)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/1811.12833/) (2018)
- [Learning to Adapt Structured Output Space for Semantic Segmentation](https://mlsanity.com/paper/1802.10349/) (2018)
- [Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation](https://mlsanity.com/paper/1909.11065/) (2019)
- [Intriguing Properties of Vision Transformers](https://mlsanity.com/paper/2105.10497/) (2021)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [Confidence Regularized Self-Training](https://mlsanity.com/paper/1908.09822/) (2019)
- [Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net](https://mlsanity.com/paper/1807.09441/) (2018)
- [DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation](https://mlsanity.com/paper/2111.14887/) (2021)

## Built on by (most cited first, 26 of 26)

- [DGInStyle: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Control](https://mlsanity.com/paper/2312.03048/) (2023)
- [SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance](https://mlsanity.com/paper/2311.16241/) (2023)
- [Collaborating Foundation Models for Domain Generalized Semantic Segmentation](https://mlsanity.com/paper/2312.09788/) (2023)
- [Generalization by Adaptation: Diffusion-Based Domain Extension for Domain-Generalized Semantic Segmentation](https://mlsanity.com/paper/2312.01850/) (2023)
- [CrossEarth: Geospatial Vision Foundation Model for Domain Generalizable Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2410.22629/) (2024)
- [Strong but simple: A Baseline for Domain Generalized Dense Perception by CLIP-based Transfer Learning](https://mlsanity.com/paper/2312.02021/) (2023)
- [EDAPS: Enhanced Domain-Adaptive Panoptic Segmentation](https://mlsanity.com/paper/2304.14291/) (2023)
- [MICDrop: Masking Image and Depth Features via Complementary Dropout for Domain-Adaptive Semantic Segmentation](https://mlsanity.com/paper/2408.16478/) (2024)
- [Exploring the Benefits of Vision Foundation Models for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2406.09896/) (2024)
- [Language-Guided Instance-Aware Domain-Adaptive Panoptic Segmentation](https://mlsanity.com/paper/2404.03799/) (2024)
- [MC-PanDA: Mask Confidence for Panoptic Domain Adaptation](https://mlsanity.com/paper/2407.14110/) (2024)
- [DUDA: Distilled Unsupervised Domain Adaptation for Lightweight Semantic Segmentation](https://mlsanity.com/paper/2504.09814/) (2025)
- [ED-SAM: An Efficient Diffusion Sampling Approach to Domain Generalization in Vision-Language Foundation Models](https://mlsanity.com/paper/2406.01432/) (2024)
- [Let Synthetic Data Shine: Domain Reassembly and Soft-Fusion for Single Domain Generalization](https://mlsanity.com/paper/2503.13617/) (2025)
- [Rein++: Efficient Generalization and Adaptation for Semantic Segmentation with Vision Foundation Models](https://mlsanity.com/paper/2508.01667/) (2025)
- [ROAD-Waymo: A Large-Scale Action Awareness Dataset for Autonomous Driving](https://mlsanity.com/paper/2411.01683/) (2024)
- [Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances](https://mlsanity.com/paper/2412.16592/) (2024)
- [MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking](https://mlsanity.com/paper/2505.24026/) (2025)
- [Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation](https://mlsanity.com/paper/2507.11955/) (2025)
- [GrabDAE: An Innovative Framework for Unsupervised Domain Adaptation Utilizing Grab-Mask and Denoise Auto-Encoder](https://mlsanity.com/paper/2410.08023/) (2024)
- [A Study on Unsupervised Domain Adaptation for Semantic Segmentation in the Era of Vision-Language Models](https://mlsanity.com/paper/2411.16407/) (2024)
- [Bridging Clear and Adverse Driving Conditions](https://mlsanity.com/paper/2508.13592/) (2025)
- [Masked Representation Modeling for Domain-Adaptive Segmentation](https://mlsanity.com/paper/2509.13801/) (2025)
- [Domain Generalization for Semantic Segmentation: A Survey](https://mlsanity.com/paper/2510.03540/) (2025)
- [Generalizable Knowledge Distillation from Vision Foundation Models for Semantic Segmentation](https://mlsanity.com/paper/2603.02554/) (2026)
- [AirMoE: Statistic-Augmented Over-the-Air MoE for Collaborative Intelligence](https://mlsanity.com/paper/2607.16562/) (2026)

## Abstract (arXiv)

Unsupervised domain adaptation (UDA) and domain generalization (DG) enable machine learning models trained on a source domain to perform well on unlabeled or even unseen target domains. As previous UDA&DG semantic segmentation methods are mostly based on outdated networks, we benchmark more recent architectures, reveal the potential of Transformers, and design the DAFormer network tailored for UDA&DG. It is enabled by three training strategies to avoid overfitting to the source domain: While (1) Rare Class Sampling mitigates the bias toward common source domain classes, (2) a Thing-Class ImageNet Feature Distance and (3) a learning rate warmup promote feature transfer from ImageNet pretraining. As UDA&DG are usually GPU memory intensive, most previous methods downscale or crop images. However, low-resolution predictions often fail to preserve fine details while models trained with cropped images fall short in capturing long-range, domain-robust context information. Therefore, we propose HRDA, a multi-resolution framework for UDA&DG, that combines the strengths of small high-resolution crops to preserve fine segmentation details and large low-resolution crops to capture long-range context dependencies with a learned scale attention. DAFormer and HRDA significantly improve the state-of-the-art UDA&DG by more than 10 mIoU on 5 different benchmarks. The implementation is available at https://github.com/lhoyer/HRDA.