Data to 7 Oct 2026

Bidirectional Learning for Domain Adaptation of Semantic Segmentation

arXiv 1904.10620 ↗First posted 24 Apr 2019cs.CVcited by 308

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Abstract (arXiv)

Domain adaptation for semantic image segmentation is very necessary since manually labeling large datasets with pixel-level labels is expensive and time consuming. Existing domain adaptation techniques either work on limited datasets, or yield not so good performance compared with supervised learning. In this paper, we propose a novel bidirectional learning framework for domain adaptation of segmentation. Using the bidirectional learning, the image translation model and the segmentation adaptation model can be learned alternatively and promote to each other. Furthermore, we propose a self-supervised learning algorithm to learn a better segmentation adaptation model and in return improve the image translation model. Experiments show that our method is superior to the state-of-the-art methods in domain adaptation of segmentation with a big margin. The source code is available at https://github.com/liyunsheng13/BDL.

Key facts. Cited by 308 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Apr 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 110; 2022: 86; 2023: 56; 2024: 38; 2025: 15; 2026: 3.

308 papers.

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2021DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentationcited by 299
2021Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentationcited by 268
2021DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motioncited by 213
2022MIC: Masked Image Consistency for Context-Enhanced Domain Adaptationcited by 176
2021FSDR: Frequency Space Domain Randomization for Domain Generalizationcited by 163
2022HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentationcited by 163
2021Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Datacited by 152
2021Self-supervised Augmentation Consistency for Adapting Semantic Segmentationcited by 149
2021Unsupervised Domain Adaptation of Object Detectors: A Surveycited by 93
2022SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentationcited by 87
2021Generalize then Adapt: Source-Free Domain Adaptive Semantic Segmentationcited by 81
2021DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentationcited by 80

38 more

2021Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimationcited by 80
2021PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Trainingcited by 80
2021Context-Aware Mixup for Domain Adaptive Semantic Segmentationcited by 79
2022Semantic-Aware Domain Generalized Segmentationcited by 78
2022WildNet: Learning Domain Generalized Semantic Segmentation from the Wildcited by 75
2022Bending Reality: Distortion-aware Transformers for Adapting to Panoramic Semantic Segmentationcited by 71
2021Category Contrast for Unsupervised Domain Adaptation in Visual Taskscited by 65
2022PODA: Prompt-driven Zero-shot Domain Adaptationcited by 60
2022Semi-supervised Semantic Segmentation with Error Localization Networkcited by 57
2021Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentationcited by 56
2021From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimationcited by 48
2021RDA: Robust Domain Adaptation via Fourier Adversarial Attackingcited by 48
2023Synthetic Datasets for Autonomous Driving: A Surveycited by 48
2021Zero-Shot Day-Night Domain Adaptation with a Physics Priorcited by 47
2021Learning Cross-modal Contrastive Features for Video Domain Adaptationcited by 47
2022Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentationcited by 47
2021Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentationcited by 46
2022A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Predictioncited by 46
2022Behind Every Domain There is a Shift: Adapting Distortion-aware Vision Transformers for Panoramic Semantic Segmentationcited by 46
2021Source-Free Domain Adaptation for Image Segmentationcited by 45
2021Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentationcited by 44
2021MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentationcited by 43
2021Multi-Target Domain Adaptation with Collaborative Consistency Learningcited by 43
2021Source-Free Open Compound Domain Adaptation in Semantic Segmentationcited by 43
2021Your Classifier can Secretly Suffice Multi-Source Domain Adaptationcited by 42
2021Cross-View Regularization for Domain Adaptive Panoptic Segmentationcited by 41
2021ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenationcited by 41
2022PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentationcited by 41
2021Cross-modal Learning for Domain Adaptation in 3D Semantic Segmentationcited by 40
2021Spectral Unsupervised Domain Adaptation for Visual Recognitioncited by 40
2022Cross-Domain Correlation Distillation for Unsupervised Domain Adaptation in Nighttime Semantic Segmentationcited by 40
2022Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentationcited by 40
2023All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenescited by 40
2021Deep Symmetric Adaptation Network for Cross-modality Medical Image Segmentationcited by 39
2021Transporting Causal Mechanisms for Unsupervised Domain Adaptationcited by 38
2023Look at the Neighbor: Distortion-aware Unsupervised Domain Adaptation for Panoramic Semantic Segmentationcited by 37
2021Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularizationcited by 36
2023One-shot Unsupervised Domain Adaptation with Personalized Diffusion Modelscited by 35

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