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SCI."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Weakly Supervised Semantic Segmentation (WSSS) techniques frequently rely on pseudo-labels to train segmentation models in the absence of fully annotated data, thereby reducing annotation costs. However, their performance is highly sensitive to the quality and uncertainty of the pseudo-labels employed. In this extended study, we further investigate the effectiveness of integrating cross-supervision and contrastive learning over pixel-level pseudo-annotations in weakly supervised settings where only image-level labels are available. We revisit CSRM, a weakly supervised segmentation framework based on a multi-branch deep convolutional network. CSRM exploits reliable pseudo-labels to mutually enhance classification and segmentation tasks, while incorporating both reliable and unreliable pseudo-labels into a contrastive representation learning scheme. In addition to standard benchmarks, this extended version evaluates CSRM on the HPA Single-Cell Classification dataset, a genuinely weakly supervised instance segmentation benchmark for protein localization in single cells. Empirical results demonstrate that CSRM achieves competitive performance on Pascal VOC 2012 (75.0% mIoU), MS COCO 2014 (50.4% mIoU), and yields substantial improvements over the baseline on the HPA dataset.<\/jats:p>","DOI":"10.1007\/s42979-026-05041-1","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T11:59:31Z","timestamp":1779278371000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CSRM: Cross-Supervision Relational Model of Pixel-Level Pseudo-Labels"],"prefix":"10.1007","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8793-7300","authenticated-orcid":false,"given":"Lucas","family":"David","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juliana","family":"Midlej","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Helio","family":"Pedrini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zanoni","family":"Dias","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"key":"5041_CR1","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1016\/j.neucom.2022.01.005","volume":"493","author":"Y Mo","year":"2022","unstructured":"Mo Y, Wu Y, Yang X, Liu F, Liao Y. 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