{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T19:00:32Z","timestamp":1782586832106,"version":"3.54.5"},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100007847","name":"Jilin Provincial Natural Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007847","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.patcog.2026.113390","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T07:41:25Z","timestamp":1772178085000},"page":"113390","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Target self-guided framework for unsupervised domain adaptation"],"prefix":"10.1016","volume":"177","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1498-5501","authenticated-orcid":false,"given":"Jingyao","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1648-8138","authenticated-orcid":false,"given":"Zhanshan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8081-4498","authenticated-orcid":false,"given":"Shuai","family":"L\u00fc","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.patcog.2026.113390_bib0001","doi-asserted-by":"crossref","unstructured":"Z. Yue, H. Zhang, Q. Sun, Make the U in UDA matter: invariant consistency learning for unsupervised domain adaptation, in: Adv. Neural Inform. Process. Syst. 36, 2023, pp. 26991\u201327004.","DOI":"10.52202\/075280-1173"},{"key":"10.1016\/j.patcog.2026.113390_bib0002","series-title":"IEEE Conf. Comput. Vis. Pattern Recog.","first-page":"7191","article-title":"Safe self-refinement for transformer-based domain adaptation","author":"Sun","year":"2022"},{"key":"10.1016\/j.patcog.2026.113390_bib0003","article-title":"Bridging domain spaces for unsupervised domain adaptation","volume":"164","author":"Jung","year":"2025","journal-title":"Pattern Recogn."},{"key":"10.1016\/j.patcog.2026.113390_bib0004","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111512","article-title":"Iterative knowledge distillation and pruning for model compression in unsupervised domain adaptation","volume":"164","author":"Wang","year":"2025","journal-title":"Pattern Recogn."},{"key":"10.1016\/j.patcog.2026.113390_bib0005","unstructured":"M. Long, H. Zhu, J. Wang, M.I. Jordan, Deep transfer learning with joint adaptation networks, in: Proc. Int. Conf. Mach. Learn. 70, 2017, pp. 2208\u20132217."},{"key":"10.1016\/j.patcog.2026.113390_bib0006","series-title":"IEEE Conf. Comput. Vis. Pattern Recog.","first-page":"3937","article-title":"Cross-domain gradient discrepancy minimization for unsupervised domain adaptation","author":"Du","year":"2021"},{"key":"10.1016\/j.patcog.2026.113390_bib0007","unstructured":"M. Tan, Q.V. Le, EfficientNet: rethinking model scaling for convolutional neural networks, in: Proc. Int. Conf. Mach. Learn. 97, 2019, pp. 6105\u20136114."},{"key":"10.1016\/j.patcog.2026.113390_bib0008","unstructured":"H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, H. J\u00e9gou, Training data-efficient image transformers & distillation through attention, in: Proc. Int. Conf. Mach. Learn. 139, 2021, pp. 10347\u201310357."},{"key":"10.1016\/j.patcog.2026.113390_bib0009","series-title":"Int. Conf. Learn. Represent.","article-title":"An image is worth 16x16 words: transformers for image recognition at scale","author":"Dosovitskiy","year":"2021"},{"key":"10.1016\/j.patcog.2026.113390_bib0010","unstructured":"L. Zhu, B. Liao, Q. Zhang, X. Wang, W. Liu, X. Wang, Vision Mamba: efficient visual representation learning with bidirectional state space model, in: Proc. Int. Conf. Mach. Learn. 235, 2024."},{"key":"10.1016\/j.patcog.2026.113390_bib0011","series-title":"Brit. Mach. Vis. Conf.","article-title":"PlainMamba: improving non-hierarchical mamba in visual recognition","author":"Yang","year":"2024"},{"key":"10.1016\/j.patcog.2026.113390_bib0012","series-title":"Adv. Neural Inform. Process. Syst.","article-title":"Multi-scale VMamba: hierarchy in hierarchy visual state space model","author":"Shi","year":"2024"},{"key":"10.1016\/j.patcog.2026.113390_bib0013","series-title":"Proc. Int. Joint Conf. Artif. Intell.","first-page":"819","article-title":"Independent feature decomposition and instance alignment for unsupervised domain adaptation","author":"He","year":"2023"},{"key":"10.1016\/j.patcog.2026.113390_bib0014","series-title":"Int. Conf. Learn. Represent.","article-title":"CDTrans: cross-domain transformer for unsupervised domain adaptation","author":"Xu","year":"2022"},{"key":"10.1016\/j.patcog.2026.113390_bib0015","series-title":"IEEE Conf. Comput. Vis. Pattern Recog.","first-page":"3561","article-title":"Patch-mix transformer for unsupervised domain adaptation: a game perspective","author":"Zhu","year":"2023"},{"key":"10.1016\/j.patcog.2026.113390_bib0016","series-title":"Proc. IEEE Workshop Appl. Comput. Vis.","first-page":"520","article-title":"TVT: transferable vision transformer for unsupervised domain adaptation","author":"Yang","year":"2023"},{"key":"10.1016\/j.patcog.2026.113390_bib0017","unstructured":"H. Rangwani, S.K. Aithal, M. Mishra, A. Jain, R.V. Babu, A closer look at smoothness in domain adversarial training, in: Proc. Int. Conf. Mach. Learn. 162, 2022, pp. 18378\u201318399."},{"key":"10.1016\/j.patcog.2026.113390_bib0018","doi-asserted-by":"crossref","unstructured":"B. Sun, J. Feng, K. Saenko, Return of frustratingly easy domain adaptation, in: Proc. AAAI Conf. Artif. Intell. 30, 2016, pp. 2058\u20132065.","DOI":"10.1609\/aaai.v30i1.10306"},{"key":"10.1016\/j.patcog.2026.113390_bib0019","doi-asserted-by":"crossref","unstructured":"Z. Xiao, H. Wang, Y. Jin, L. Feng, G. Chen, F. Huang, J. Zhao, SPA: a graph spectral alignment perspective for domain adaptation, in: Adv. Neural Inform. Process. Syst. 36, 2023, pp. 37252\u201337272.","DOI":"10.52202\/075280-1619"},{"key":"10.1016\/j.patcog.2026.113390_bib0020","first-page":"7714","article-title":"CPSR-CLIP: conditional prompt-induced style reconstruction for zero-shot domain adaptation","volume":"27","author":"Qian","year":"2025","journal-title":"IEEE TMM."},{"key":"10.1016\/j.patcog.2026.113390_bib0021","first-page":"5731","article-title":"Multiple adaptation network for multi-source and multi-target domain adaptation","volume":"27","author":"Lu","year":"2025","journal-title":"IEEE TMM."},{"key":"10.1016\/j.patcog.2026.113390_bib0022","series-title":"Semantic dual-adversarial network for blended-target domain adaptation","first-page":"7930","volume":"27","author":"Lu","year":"2025"},{"key":"10.1016\/j.patcog.2026.113390_bib0023","series-title":"IEEE Conf. Comput. Vis. Pattern Recog.","first-page":"30588","article-title":"ADA: adaptive detection of unknown categories in black-box domain adaptation","author":"Lai","year":"2025"},{"key":"10.1016\/j.patcog.2026.113390_bib0024","series-title":"IEEE Conf. Comput. Vis. Pattern Recog.","first-page":"4916","article-title":"Link-based contrastive learning for one-shot unsupervised domain adaptation","author":"Zhang","year":"2025"},{"key":"10.1016\/j.patcog.2026.113390_bib0025","unstructured":"A. Radford, J.W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, I. Sutskever, Learning transferable visual models from natural language supervision, in: Proc. Int. Conf. Mach. Learn. 139, 2021, pp. 8748\u20138763."},{"key":"10.1016\/j.patcog.2026.113390_bib0026","series-title":"IEEE Conf. Comput. Vis. Pattern Recog.","first-page":"23375","article-title":"Domain-agnostic mutual prompting for unsupervised domain adaptation","author":"Du","year":"2024"},{"key":"10.1016\/j.patcog.2026.113390_bib0027","series-title":"Domain adaptation via prompt learning","first-page":"1","author":"Ge","year":"2023"},{"key":"10.1016\/j.patcog.2026.113390_bib0028","series-title":"Int. Conf. Comput. Vis. Worksh.","first-page":"4355","article-title":"AD-CLIP: adapting domains in prompt space using CLIP","author":"Singha","year":"2023"},{"key":"10.1016\/j.patcog.2026.113390_bib0029","series-title":"Int. Conf. Comput. Vis.","first-page":"16155","article-title":"PADCLIP: pseudo-labeling with adaptive debiasing in CLIP for unsupervised domain adaptation","author":"Lai","year":"2023"},{"key":"10.1016\/j.patcog.2026.113390_bib0030","first-page":"729","article-title":"Prompt-based distribution alignment for unsupervised domain adaptation","volume":"38","author":"Bai","year":"2024","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.patcog.2026.113390_bib0031","series-title":"IEEE Conf. Comput. Vis. Pattern Recog.","first-page":"23364","article-title":"Split to merge: unifying separated modalities for unsupervised domain adaptation","author":"Li","year":"2024"},{"key":"10.1016\/j.patcog.2026.113390_bib0032","series-title":"RSMamba: remote sensing image classification with state space model","author":"Chen","year":"2024"},{"key":"10.1016\/j.patcog.2026.113390_bib0033","series-title":"Adv. Neural Inform. Process. Syst.","article-title":"Hybrid mamba for few-shot segmentation","author":"Xu","year":"2024"},{"key":"10.1016\/j.patcog.2026.113390_bib0034","series-title":"IEEE Conf. Comput. Vis. Pattern Recog.","article-title":"MambaVision: a hybrid mamba-transformer vision backbone","author":"Hatamizadeh","year":"2025"},{"key":"10.1016\/j.patcog.2026.113390_bib0035","series-title":"IEEE Conf. Comput. Vis. Pattern Recog. Worksh.","article-title":"Randaugment: practical automated data augmentation with a reduced search space","author":"Cubuk","year":"2020"},{"key":"10.1016\/j.patcog.2026.113390_bib0036","unstructured":"K. Sohn, D. Berthelot, C.-L. Li, Z. Zhang, N. Carlini, E.D. Cubuk, A. Kurakin, H. Zhang, C. Raffel, FixMatch: simplifying semi-supervised learning with consistency and confidence, in: Adv. Neural Inform. Process. Syst. 33, 2020, pp. 596\u2013608."},{"key":"10.1016\/j.patcog.2026.113390_bib0037","series-title":"IEEE Conf. Comput. Vis. Pattern Recog.","first-page":"28545","article-title":"Learning CNN on ViT: a hybrid model to explicitly class-specific boundaries for domain adaptation","author":"Ngo","year":"2024"},{"key":"10.1016\/j.patcog.2026.113390_bib0038","series-title":"Int. Conf. Learn. Represent.","article-title":"Mixup: beyond empirical risk minimization","author":"Zhang","year":"2018"},{"key":"10.1016\/j.patcog.2026.113390_bib0039","series-title":"Int. Conf. Comput. Vis.","first-page":"6023","article-title":"CutMix: regularization strategy to train strong classifiers with localizable features","author":"Yun","year":"2019"},{"key":"10.1016\/j.patcog.2026.113390_bib0040","series-title":"IEEE Conf. Comput. Vis. Pattern Recog.","first-page":"5018","article-title":"Deep hashing network for unsupervised domain adaptation","author":"Venkateswara","year":"2017"},{"key":"10.1016\/j.patcog.2026.113390_bib0041","series-title":"Int. Conf. Comput. Vis.","first-page":"1406","article-title":"Moment matching for multi-source domain adaptation","author":"Peng","year":"2019"},{"key":"10.1016\/j.patcog.2026.113390_bib0042","unstructured":"X. Peng, B. Usman, N. Kaushik, J. Hoffman, D. Wang, K. Saenko, Visda: the visual domain adaptation challenge, 2017. arXiv: 1710.06924."},{"key":"10.1016\/j.patcog.2026.113390_bib0043","series-title":"Eur. Conf. Comput. Vis.","first-page":"213","article-title":"Adapting visual category models to new domains","author":"Saenko","year":"2010"},{"key":"10.1016\/j.patcog.2026.113390_bib0044","series-title":"ACM Int. Conf. Multimedia","first-page":"5620","article-title":"Making the best of both worlds: a domain-oriented transformer for unsupervised domain adaptation","author":"Ma","year":"2022"}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326003559?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326003559?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T16:45:14Z","timestamp":1777567514000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326003559"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":44,"alternative-id":["S0031320326003559"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113390","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Target self-guided framework for unsupervised domain adaptation","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113390","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"113390"}}