# Domain Adaptive Semantic Segmentation via Regional Contrastive Consistency Regularization

arXiv: https://arxiv.org/abs/2110.05170 · HTML: https://mlsanity.com/paper/2110.05170/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 28 references that are in the map.
- First posted on arXiv 11 Oct 2021.

## Builds on (most cited first, 21 of 21)

- [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)
- [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016)
- [Learning to Adapt Structured Output Space for Semantic Segmentation](https://mlsanity.com/paper/1802.10349/) (2018)
- [FDA: Fourier Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2004.05498/) (2020)
- [Confidence Regularized Self-Training](https://mlsanity.com/paper/1908.09822/) (2019)
- [Bidirectional Learning for Domain Adaptation of Semantic Segmentation](https://mlsanity.com/paper/1904.10620/) (2019)
- [Exploring Cross-Image Pixel Contrast for Semantic Segmentation](https://mlsanity.com/paper/2101.11939/) (2021)
- [DACS: Domain Adaptation via Cross-domain Mixed Sampling](https://mlsanity.com/paper/2007.08702/) (2020)
- [Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision](https://mlsanity.com/paper/2004.07703/) (2020)
- [Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation](https://mlsanity.com/paper/2003.00867/) (2020)
- [Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation](https://mlsanity.com/paper/2007.09222/) (2020)
- [Self-Ensembling with GAN-based Data Augmentation for Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/1909.00589/) (2019)
- [Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation](https://mlsanity.com/paper/2003.08040/) (2020)
- [Semi-supervised Semantic Segmentation with Directional Context-aware Consistency](https://mlsanity.com/paper/2106.14133/) (2021)
- [Contextual-Relation Consistent Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2007.02424/) (2020)
- [Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/2003.04614/) (2020)
- [Domain Adaptive Semantic Segmentation Using Weak Labels](https://mlsanity.com/paper/2007.15176/) (2020)
- [Affinity Space Adaptation for Semantic Segmentation Across Domains](https://mlsanity.com/paper/2009.12559/) (2020)
- [BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation](https://mlsanity.com/paper/2108.03267/) (2021)
- [Domain Adaptation for Semantic Segmentation via Patch-Wise Contrastive Learning](https://mlsanity.com/paper/2104.11056/) (2021)

## Built on by (most cited first, 16 of 16)

- [Instance-Aware Domain Generalization for Face Anti-Spoofing](https://mlsanity.com/paper/2304.05640/) (2023)
- [PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation](https://mlsanity.com/paper/2211.07609/) (2022)
- [Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving](https://mlsanity.com/paper/2304.11928/) (2023)
- [DGMamba: Domain Generalization via Generalized State Space Model](https://mlsanity.com/paper/2404.07794/) (2024)
- [Rethinking Domain Generalization: Discriminability and Generalizability](https://mlsanity.com/paper/2309.16483/) (2023)
- [BA-SAM: Scalable Bias-Mode Attention Mask for Segment Anything Model](https://mlsanity.com/paper/2401.02317/) (2024)
- [LiDAR-UDA: Self-ensembling Through Time for Unsupervised LiDAR Domain Adaptation](https://mlsanity.com/paper/2309.13523/) (2023)
- [Augmentation-based Domain Generalization for Semantic Segmentation](https://mlsanity.com/paper/2304.12122/) (2023)
- [Diverse Target and Contribution Scheduling for Domain Generalization](https://mlsanity.com/paper/2309.16460/) (2023)
- [Probabilistic Contrastive Learning for Domain Adaptation](https://mlsanity.com/paper/2111.06021/) (2021)
- [Self-Training Guided Disentangled Adaptation for Cross-Domain Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2301.05526/) (2023)
- [Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation](https://mlsanity.com/paper/2302.06992/) (2023)
- [Adversarial Attacks on Both Face Recognition and Face Anti-spoofing Models](https://mlsanity.com/paper/2405.16940/) (2024)
- [PiPa++: Towards Unification of Domain Adaptive Semantic Segmentation via Self-supervised Learning](https://mlsanity.com/paper/2407.17101/) (2024)
- [Source-Free Test-Time Adaptation For Online Surface-Defect Detection](https://mlsanity.com/paper/2408.09494/) (2024)
- [DDS-UDA: Dual-Domain Synergy for Unsupervised Domain Adaptation in Joint Segmentation of Optic Disc and Optic Cup](https://mlsanity.com/paper/2603.13345/) (2026)

## Abstract (arXiv)

Unsupervised domain adaptation (UDA) for semantic segmentation has been well-studied in recent years. However, most existing works largely neglect the local regional consistency across different domains and are less robust to changes in outdoor environments. In this paper, we propose a novel and fully end-to-end trainable approach, called regional contrastive consistency regularization (RCCR) for domain adaptive semantic segmentation. Our core idea is to pull the similar regional features extracted from the same location of different images, i.e., the original image and augmented image, to be closer, and meanwhile push the features from the different locations of the two images to be separated. We innovatively propose a region-wise contrastive loss with two sampling strategies to realize effective regional consistency. Besides, we present momentum projection heads, where the teacher projection head is the exponential moving average of the student. Finally, a memory bank mechanism is designed to learn more robust and stable region-wise features under varying environments. Extensive experiments on two common UDA benchmarks, i.e., GTAV to Cityscapes and SYNTHIA to Cityscapes, demonstrate that our approach outperforms the state-of-the-art methods.