{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:19:19Z","timestamp":1760059159660,"version":"build-2065373602"},"reference-count":71,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T00:00:00Z","timestamp":1748217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62002053","62271130","2023A1515010066","2022ZDZX3042","2024B2021"],"award-info":[{"award-number":["62002053","62271130","2023A1515010066","2022ZDZX3042","2024B2021"]}]},{"name":"Natural Science Foundation of Guangdong Province","award":["62002053","62271130","2023A1515010066","2022ZDZX3042","2024B2021"],"award-info":[{"award-number":["62002053","62271130","2023A1515010066","2022ZDZX3042","2024B2021"]}]},{"DOI":"10.13039\/501100003453","name":"Key Area Special Fund of Guangdong Provincial Department of Education","doi-asserted-by":"publisher","award":["62002053","62271130","2023A1515010066","2022ZDZX3042","2024B2021"],"award-info":[{"award-number":["62002053","62271130","2023A1515010066","2022ZDZX3042","2024B2021"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Social Public Welfare and Basic Research Project of Zhongshan City","award":["62002053","62271130","2023A1515010066","2022ZDZX3042","2024B2021"],"award-info":[{"award-number":["62002053","62271130","2023A1515010066","2022ZDZX3042","2024B2021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Unsupervised domain adaptation (UDA) effectively transfers knowledge learned from a labeled source domain to an unlabeled target domain. The teacher\u2013student framework, which generates pseudo-labels for target domain samples and uses them for pseudo-supervised training, enables self-training and improves generalization in UDA object detection. However, for one-stage detection models, pseudo-labels are unreliable when positive and negative samples are imbalanced. This may lead the model to overfit the source domain and overlook important target-domain information. In this work, we propose a novel domain-specific student\u2013teacher framework to address this issue. The innovations of the proposed framework can be summarized in two aspects. First, we employ two domain-specific heads (DSHs) in the student model to handle inputs from the source domain and the target domain separately. These two heads are optimized independently with samples from their respective domains. This design allows for reducing the impact of unreliable pseudo-labels and fully leveraging unique information specific to the target domain. Second, we introduce an auxiliary reconstruction branch, named the multi-scale mask adversarial alignment (MMAA) module, into the teacher\u2013student framework. The MMAA is tasked with reconstructing randomly masked multi-scale features of the source domain, which enhances the student model\u2019s semantic representation capability and facilitates the generation of high-quality pseudo-labels. Experimental results on six diverse cross-domain scenarios demonstrate the effectiveness of our framework.<\/jats:p>","DOI":"10.3390\/info16060439","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T11:12:57Z","timestamp":1748344377000},"page":"439","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Reconstructing Domain-Specific Features for Unsupervised Domain-Adaptive Object Detection"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9321-5064","authenticated-orcid":false,"given":"Shuai","family":"Dong","sequence":"first","affiliation":[{"name":"Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528400, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kang","family":"Deng","sequence":"additional","affiliation":[{"name":"Mathematic and Information Institute, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Zou","sequence":"additional","affiliation":[{"name":"Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528400, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,26]]},"reference":[{"key":"ref_1","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton","year":"2012","journal-title":"J. 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