Universal Domain Adaptation for Semantic Segmentation

• URL: http://arxiv.org/abs/2505.22458v2
• Date: Thu, 05 Jun 2025 22:38:25 GMT
• Title: Universal Domain Adaptation for Semantic Segmentation
• Authors: Seun-An Choe, Keon-Hee Park, Jinwoo Choi, Gyeong-Moon Park,
• Abstract summary: Unsupervised domain adaptation for semantic segmentation (UDA-SS) aims to transfer knowledge from labeled source data to unlabeled target data.<n>Traditional UDA-SS methods assume that category settings between source and target domains are known, which is unrealistic in real-world scenarios.<n>We propose Universal Domain Adaptation for Semantic (UniDA-SS) to achieve robust adaptation even without prior knowledge of category settings.
• Score: 6.860995924860749
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Unsupervised domain adaptation for semantic segmentation (UDA-SS) aims to transfer knowledge from labeled source data to unlabeled target data. However, traditional UDA-SS methods assume that category settings between source and target domains are known, which is unrealistic in real-world scenarios. This leads to performance degradation if private classes exist. To address this limitation, we propose Universal Domain Adaptation for Semantic Segmentation (UniDA-SS), achieving robust adaptation even without prior knowledge of category settings. We define the problem in the UniDA-SS scenario as low confidence scores of common classes in the target domain, which leads to confusion with private classes. To solve this problem, we propose UniMAP: UniDA-SS with Image Matching and Prototype-based Distinction, a novel framework composed of two key components. First, Domain-Specific Prototype-based Distinction (DSPD) divides each class into two domain-specific prototypes, enabling finer separation of domain-specific features and enhancing the identification of common classes across domains. Second, Target-based Image Matching (TIM) selects a source image containing the most common-class pixels based on the target pseudo-label and pairs it in a batch to promote effective learning of common classes. We also introduce a new UniDA-SS benchmark and demonstrate through various experiments that UniMAP significantly outperforms baselines. The code is available at https://github.com/KU-VGI/UniMAP.

Related papers

• Tackling Dimensional Collapse toward Comprehensive Universal Domain Adaptation [11.875619863954238]
  Universal Domain Adaptation (UniDA) addresses unsupervised domain adaptation where target classes may differ arbitrarily from source ones, except for a shared subset.<n>An important approach, partial domain matching (PDM), aligns only shared classes but struggles in extreme cases where many source classes are absent in the target domain, underperforming the most naive baseline that trains on only source data.<n>We propose to jointly leverage the alignment and uniformity techniques in modern self-supervised learning (SSL) on the unlabeled target data to preserve the intrinsic structure of the learned representations.
  arXiv  Detail & Related papers  (2024-10-15T04:51:37Z)
• Open-Set Domain Adaptation for Semantic Segmentation [6.3951361316638815]
  We introduce Open-Set Domain Adaptation for Semantic (OSDA-SS) for the first time, where the target domain includes unknown classes. To address these issues, we propose Boundary and Unknown Shape-Aware open-set domain adaptation, coined BUS. Our BUS can accurately discern the boundaries between known and unknown classes in a contrastive manner using a novel dilation-erosion-based contrastive loss.
  arXiv  Detail & Related papers  (2024-05-30T09:55:19Z)
• Unified Optimal Transport Framework for Universal Domain Adaptation [27.860165056943796]
  Universal Domain Adaptation (UniDA) aims to transfer knowledge from a source domain to a target domain without any constraints on label sets. Most existing methods require manually specified or hand-tuned threshold values to detect common samples. We propose to use Optimal Transport (OT) to handle these issues under a unified framework, namely UniOT.
  arXiv  Detail & Related papers  (2022-10-31T05:07:09Z)
• Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation [52.63046674453461]
  Prototypical Contrast Adaptation (ProCA) is a contrastive learning method for unsupervised domain adaptive semantic segmentation. ProCA incorporates inter-class information into class-wise prototypes, and adopts the class-centered distribution alignment for adaptation.
  arXiv  Detail & Related papers  (2022-07-14T04:54:26Z)
• Semantic-Aware Domain Generalized Segmentation [67.49163582961877]
  Deep models trained on source domain lack generalization when evaluated on unseen target domains with different data distributions. We propose a framework including two novel modules: Semantic-Aware Normalization (SAN) and Semantic-Aware Whitening (SAW) Our approach shows significant improvements over existing state-of-the-art on various backbone networks.
  arXiv  Detail & Related papers  (2022-04-02T09:09:59Z)
• Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation [91.30558794056056]
  Unsupervised domain adaptation (UDA) for semantic segmentation has been attracting attention recently. We present a novel framework based on three main design principles: discover, hallucinate, and adapt. We evaluate our solution on standard benchmark GTA to C-driving, and achieved new state-of-the-art results.
  arXiv  Detail & Related papers  (2021-10-08T13:20:09Z)
• Cross-domain Contrastive Learning for Unsupervised Domain Adaptation [108.63914324182984]
  Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source domain to a different unlabeled target domain. We build upon contrastive self-supervised learning to align features so as to reduce the domain discrepancy between training and testing sets.
  arXiv  Detail & Related papers  (2021-06-10T06:32:30Z)
• OVANet: One-vs-All Network for Universal Domain Adaptation [78.86047802107025]
  Existing methods manually set a threshold to reject unknown samples based on validation or a pre-defined ratio of unknown samples. We propose a method to learn the threshold using source samples and to adapt it to the target domain. Our idea is that a minimum inter-class distance in the source domain should be a good threshold to decide between known or unknown in the target.
  arXiv  Detail & Related papers  (2021-04-07T18:36:31Z)
• Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation [91.58443042554903]
  We propose an end-to-end Prototypical Cross-domain Self-Supervised Learning (PCS) framework for Few-shot Unsupervised Domain Adaptation (FUDA) PCS not only performs cross-domain low-level feature alignment, but it also encodes and aligns semantic structures in the shared embedding space across domains. Compared with state-of-the-art methods, PCS improves the mean classification accuracy over different domain pairs on FUDA by 10.5%, 3.5%, 9.0%, and 13.2% on Office, Office-Home, VisDA-2017, and DomainNet, respectively.
  arXiv  Detail & Related papers  (2021-03-31T02:07:42Z)
• Learning Target Domain Specific Classifier for Partial Domain Adaptation [85.71584004185031]
  Unsupervised domain adaptation (UDA) aims at reducing the distribution discrepancy when transferring knowledge from a labeled source domain to an unlabeled target domain. This paper focuses on a more realistic UDA scenario, where the target label space is subsumed to the source label space.
  arXiv  Detail & Related papers  (2020-08-25T02:28:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.