Unsupervised Domain Adaptation via Similarity-based Prototypes for Cross-Modality Segmentation

• URL: http://arxiv.org/abs/2510.20596v1
• Date: Thu, 23 Oct 2025 14:24:12 GMT
• Title: Unsupervised Domain Adaptation via Similarity-based Prototypes for Cross-Modality Segmentation
• Authors: Ziyu Ye, Chen Ju, Chaofan Ma, Xiaoyun Zhang,
• Abstract summary: We propose a novel framework for cross-modality segmentation via similarity-based prototypes.<n>In specific, we learn class-wise prototypes within an embedding space, then introduce a similarity constraint to make these prototypes representative for each semantic class.<n>We use dictionaries to store prototypes extracted from different images, which prevents the class-missing problem and enables the contrastive learning of prototypes.
• Score: 16.282091352966635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning models have achieved great success on various vision challenges, but a well-trained model would face drastic performance degradation when applied to unseen data. Since the model is sensitive to domain shift, unsupervised domain adaptation attempts to reduce the domain gap and avoid costly annotation of unseen domains. This paper proposes a novel framework for cross-modality segmentation via similarity-based prototypes. In specific, we learn class-wise prototypes within an embedding space, then introduce a similarity constraint to make these prototypes representative for each semantic class while separable from different classes. Moreover, we use dictionaries to store prototypes extracted from different images, which prevents the class-missing problem and enables the contrastive learning of prototypes, and further improves performance. Extensive experiments show that our method achieves better results than other state-of-the-art methods.

Related papers

• Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Image Segmentation [66.82598255715696]
  Federated learning enables multiple medical institutions to train a global model without sharing data.<n>Current approaches primarily focus on final-layer features, overlooking critical multi-level cues.<n>We propose FedBCS to bridge feature representation gaps via domain-invariant contextual prototypes alignment.
  arXiv  Detail & Related papers  (2025-11-14T04:15:34Z)
• Mind the Gap Between Prototypes and Images in Cross-domain Finetuning [64.97317635355124]
  We propose a contrastive prototype-image adaptation (CoPA) to adapt different transformations respectively for prototypes and images. Experiments on Meta-Dataset demonstrate that CoPA achieves the state-of-the-art performance more efficiently.
  arXiv  Detail & Related papers  (2024-10-16T11:42:11Z)
• An Enhanced Federated Prototype Learning Method under Domain Shift [36.73020712815063]
  Federated Learning (FL) allows collaborative machine learning training without sharing private data. Recent paper introduces variance-aware dual-level prototype clustering and uses a novel $alpha$-sparsity prototype loss. Evaluations on the Digit-5, Office-10, and DomainNet datasets show that our method performs better than existing approaches.
  arXiv  Detail & Related papers  (2024-09-27T09:28:27Z)
• Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification [23.82751179819225]
  We develop a novel Adaptive Parametric Prototype Learning (APPL) method under the meta-learning convention for cross-domain few-shot classification. APPL yields superior performance than many state-of-the-art cross-domain few-shot learning methods.
  arXiv  Detail & Related papers  (2023-09-04T03:58:50Z)
• Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization [20.4183741427867]
  Semi-supervised semantic segmentation requires the model to propagate the label information from limited annotated images to unlabeled ones. A challenge for such a per-pixel prediction task is the large intra-class variation. We propose a novel approach to regularize the distribution of within-class features to ease label propagation difficulty.
  arXiv  Detail & Related papers  (2022-10-10T01:38:01Z)
• Dual Prototypical Contrastive Learning for Few-shot Semantic Segmentation [55.339405417090084]
  We propose a dual prototypical contrastive learning approach tailored to the few-shot semantic segmentation (FSS) task. The main idea is to encourage the prototypes more discriminative by increasing inter-class distance while reducing intra-class distance in prototype feature space. We demonstrate that the proposed dual contrastive learning approach outperforms state-of-the-art FSS methods on PASCAL-5i and COCO-20i datasets.
  arXiv  Detail & Related papers  (2021-11-09T08:14:50Z)
• Attentional Prototype Inference for Few-Shot Segmentation [128.45753577331422]
  We propose attentional prototype inference (API), a probabilistic latent variable framework for few-shot segmentation. We define a global latent variable to represent the prototype of each object category, which we model as a probabilistic distribution. We conduct extensive experiments on four benchmarks, where our proposal obtains at least competitive and often better performance than state-of-the-art prototype-based methods.
  arXiv  Detail & Related papers  (2021-05-14T06:58:44Z)
• Cross-Domain Similarity Learning for Face Recognition in Unseen Domains [90.35908506994365]
  We introduce a novel cross-domain metric learning loss, which we dub Cross-Domain Triplet (CDT) loss, to improve face recognition in unseen domains. The CDT loss encourages learning semantically meaningful features by enforcing compact feature clusters of identities from one domain. Our method does not require careful hard-pair sample mining and filtering strategy during training.
  arXiv  Detail & Related papers  (2021-03-12T19:48:01Z)
• Part-aware Prototype Network for Few-shot Semantic Segmentation [50.581647306020095]
  We propose a novel few-shot semantic segmentation framework based on the prototype representation. Our key idea is to decompose the holistic class representation into a set of part-aware prototypes. We develop a novel graph neural network model to generate and enhance the proposed part-aware prototypes.
  arXiv  Detail & Related papers  (2020-07-13T11:03:09Z)

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.