{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T11:46:32Z","timestamp":1782819992799,"version":"3.54.5"},"reference-count":39,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Semantic segmentation in foggy scenes is crucial for autonomous driving systems, yet acquiring annotated real-world foggy data is highly costly. Existing unsupervised domain adaptation methods typically adopt a self-training strategy, adapting models trained under clear-weather conditions to unlabeled foggy target domains and constructing supervision signals via pseudo-labels. However, these methods mainly focus on improving the reliability of target-domain supervision while paying insufficient attention to class confusion caused by the degradation of class discriminability. In fact, the performance degradation of self-training in foggy scenarios is not caused by a single factor, but is jointly affected by unreliable supervision signals and reduced class discriminability. To address these issues, this paper proposes a reliable pseudo-labeling and confusion calibration framework for foggy-scene semantic segmentation, termed RPCC. Specifically, dynamic energy-guided pseudo-labeling (DEPL) models the reliability of target-domain predictions using energy scores, thereby improving the reliability of target-domain supervision signals. Furthermore, the reliable-region class confusion calibration (RCC) module models and calibrates semantic class relationships in target-domain predictions based on reliable pseudo-label supervision, thereby suppressing class confusion and enhancing semantic boundary clarity. Experimental results demonstrate that RPCC outperforms existing methods on multiple real-world foggy-scene datasets and shows favorable generalization to other adverse weather conditions.<\/jats:p>","DOI":"10.3390\/jimaging12070289","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T08:26:15Z","timestamp":1782807975000},"page":"289","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Reliable Pseudo-Labeling and Confusion Calibration for Foggy-Scene Semantic Segmentation"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0809-9360","authenticated-orcid":false,"given":"Shuai","family":"Yan","sequence":"first","affiliation":[{"name":"College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, China"},{"name":"Key Lab of Network and Information Security of Hebei Province, Shijiazhuang 050024, China"},{"name":"Hebei Engineering Research Center for Supply Chain Big Data Analysis and Data Security, Shijiazhuang 050024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shirong","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, China"},{"name":"Key Lab of Network and Information Security of Hebei Province, Shijiazhuang 050024, China"},{"name":"Hebei Engineering Research Center for Supply Chain Big Data Analysis and Data Security, Shijiazhuang 050024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhicheng","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, China"},{"name":"Key Lab of Network and Information Security of Hebei Province, Shijiazhuang 050024, China"},{"name":"Hebei Engineering Research Center for Supply Chain Big Data Analysis and Data Security, Shijiazhuang 050024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tsai, Y.H., Hung, W.C., Schulter, S., Sohn, K., Yang, M.H., and Chandraker, M. 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