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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,5,31]]},"abstract":"<jats:p>Large pre-trained vision-language models (VLMs) like CLIP have shown great potential for solving the unsupervised domain adaptation (UDA) problem. Existing prompt learning for UDA based on the unsupervised-trained VLMs requires distribution alignment between source and target domains in the common space for both vision and language branches. However, it is difficult for rough cross-domain alignment to maintain the discriminative semantic structure of both domains. Besides, the coarse features with non-informative noises due to ignoring the pseudo-label noises may cause failures to concentrate on precise semantics alignment. In this work, we propose a Prompt-Based Invertible Mapping Alignment (PIMA) method to incorporate discriminative domain knowledge into prompt learning, which is featured with refined cross-domain alignment in two separate space with a well-kept structure. Specifically, we design an invertible neural network-based homeomorphism mapping, and then achieve distribution alignment through such invertible mapping for connecting source and target visual feature space, which can preserve the data semantic structure. For better semantic alignment in vision-language space, we develop cross-modal implicit contrastive learning module to regularize non-informative features, which aims to find the low-rankness of implicit representation space. We conducted extensive experiments on three benchmark datasets to prove the advantages of our proposed PIMA over state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3725735","type":"journal-article","created":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T15:03:53Z","timestamp":1742569433000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Prompt-Based Invertible Mapping Alignment for Unsupervised Domain Adaptation"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6894-8207","authenticated-orcid":false,"given":"Chao","family":"Wen","sequence":"first","affiliation":[{"name":"The Institute of Big Data Science and Industry, Shanxi University, Taiyuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5905-0354","authenticated-orcid":false,"given":"Chen","family":"Wei","sequence":"additional","affiliation":[{"name":"The Institute of Big Data Science and Industry, Shanxi University, Taiyuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6772-4247","authenticated-orcid":false,"given":"Yuhua","family":"Qian","sequence":"additional","affiliation":[{"name":"The Institute of Big Data Science and Industry, Shanxi University, Taiyuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8049-1828","authenticated-orcid":false,"given":"Xiaodan","family":"Song","sequence":"additional","affiliation":[{"name":"Guangzhou Institute of Technology, Xidian University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7857-0845","authenticated-orcid":false,"given":"Xuemei","family":"Xie","sequence":"additional","affiliation":[{"name":"Guangzhou Institute of Technology, Xidian University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,5,22]]},"reference":[{"issue":"8","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"2953","DOI":"10.1007\/s10994-023-06357-2","article-title":"MapFlow: Latent transition via normalizing flow for unsupervised domain adaptation","volume":"112","author":"Askari Hossein","year":"2023","unstructured":"Hossein Askari, Yasir Latif, and Hongfu Sun. 2023. 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