Target Semantics Clustering via Text Representations for Robust Universal Domain Adaptation

• URL: http://arxiv.org/abs/2506.03521v1
• Date: Wed, 04 Jun 2025 03:11:53 GMT
• Title: Target Semantics Clustering via Text Representations for Robust Universal Domain Adaptation
• Authors: Weinan He, Zilei Wang, Yixin Zhang,
• Abstract summary: Universal Domain Adaptation (UniDA) focuses on transferring source domain knowledge to the target domain under both domain shift and unknown category shift.<n>Current methods typically obtain target domain semantics centers from an unconstrained continuous image representation space.<n>In this paper, based on vision-language models, we search for semantic centers in a semantically meaningful and discrete text representation space.
• Score: 37.61604558855609
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Universal Domain Adaptation (UniDA) focuses on transferring source domain knowledge to the target domain under both domain shift and unknown category shift. Its main challenge lies in identifying common class samples and aligning them. Current methods typically obtain target domain semantics centers from an unconstrained continuous image representation space. Due to domain shift and the unknown number of clusters, these centers often result in complex and less robust alignment algorithm. In this paper, based on vision-language models, we search for semantic centers in a semantically meaningful and discrete text representation space. The constrained space ensures almost no domain bias and appropriate semantic granularity for these centers, enabling a simple and robust adaptation algorithm. Specifically, we propose TArget Semantics Clustering (TASC) via Text Representations, which leverages information maximization as a unified objective and involves two stages. First, with the frozen encoders, a greedy search-based framework is used to search for an optimal set of text embeddings to represent target semantics. Second, with the search results fixed, encoders are refined based on gradient descent, simultaneously achieving robust domain alignment and private class clustering. Additionally, we propose Universal Maximum Similarity (UniMS), a scoring function tailored for detecting open-set samples in UniDA. Experimentally, we evaluate the universality of UniDA algorithms under four category shift scenarios. Extensive experiments on four benchmarks demonstrate the effectiveness and robustness of our method, which has achieved state-of-the-art performance.

Related papers

• Exploring Semantic Consistency and Style Diversity for Domain Generalized Semantic Segmentation [4.850207292777464]
  Domain Generalized Semantic aims to enhance the generalization of semantic segmentation across unknown target domains.<n>We introduce SCSD for Semantic Consistency prediction and Style Diversity generalization.<n>SCSD significantly outperforms existing state-of-theart methods.
  arXiv  Detail & Related papers  (2024-12-16T18:20:06Z)
• Generalize or Detect? Towards Robust Semantic Segmentation Under Multiple Distribution Shifts [56.57141696245328]
  In open-world scenarios, where both novel classes and domains may exist, an ideal segmentation model should detect anomaly classes for safety. Existing methods often struggle to distinguish between domain-level and semantic-level distribution shifts.
  arXiv  Detail & Related papers  (2024-11-06T11:03:02Z)
• Multimodal Unsupervised Domain Generalization by Retrieving Across the Modality Gap [11.96884248631201]
  We tackle the multimodal version of the unsupervised domain generalization problem. Our framework relies on the premise that the source dataset can be accurately and efficiently searched in a joint vision-language space. We show theoretically that cross-modal approximate nearest neighbor search suffers from low recall due to the large distance between text queries and the image centroids used for coarse quantization.
  arXiv  Detail & Related papers  (2024-02-06T21:29:37Z)
• Adaptive Betweenness Clustering for Semi-Supervised Domain Adaptation [108.40945109477886]
  We propose a novel SSDA approach named Graph-based Adaptive Betweenness Clustering (G-ABC) for achieving categorical domain alignment. Our method outperforms previous state-of-the-art SSDA approaches, demonstrating the superiority of the proposed G-ABC algorithm.
  arXiv  Detail & Related papers  (2024-01-21T09:57:56Z)
• Low-confidence Samples Matter for Domain Adaptation [47.552605279925736]
  Domain adaptation (DA) aims to transfer knowledge from a label-rich source domain to a related but label-scarce target domain. We propose a novel contrastive learning method by processing low-confidence samples. We evaluate the proposed method in both unsupervised and semi-supervised DA settings.
  arXiv  Detail & Related papers  (2022-02-06T15:45:45Z)
• 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)
• Simultaneous Semantic Alignment Network for Heterogeneous Domain Adaptation [67.37606333193357]
  We propose aSimultaneous Semantic Alignment Network (SSAN) to simultaneously exploit correlations among categories and align the centroids for each category across domains. By leveraging target pseudo-labels, a robust triplet-centroid alignment mechanism is explicitly applied to align feature representations for each category. Experiments on various HDA tasks across text-to-image, image-to-image and text-to-text successfully validate the superiority of our SSAN against state-of-the-art HDA methods.
  arXiv  Detail & Related papers  (2020-08-04T16:20:37Z)
• Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation [95.10255219396109]
  We propose a fine-grained adversarial learning strategy for class-level feature alignment. We adopt a fine-grained domain discriminator that not only plays as a domain distinguisher, but also differentiates domains at class level. An analysis with Class Center Distance (CCD) validates that our fine-grained adversarial strategy achieves better class-level alignment.
  arXiv  Detail & Related papers  (2020-07-17T20:50:59Z)
• Contextual-Relation Consistent Domain Adaptation for Semantic Segmentation [44.19436340246248]
  This paper presents an innovative local contextual-relation consistent domain adaptation technique. It aims to achieve local-level consistencies during the global-level alignment. Experiments demonstrate its superior segmentation performance as compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-07-05T19:00:46Z)
• Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation Method for Semantic Segmentation [97.8552697905657]
  A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains. We propose Alleviating Semantic-level Shift (ASS), which can successfully promote the distribution consistency from both global and local views. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes.
  arXiv  Detail & Related papers  (2020-04-02T03:25:05Z)

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.