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In this paper, we leverage the abundance of freely accessible trained models to introduce a cost-free approach to model merging. It focuses on a layer-wise integration of merged models, aiming to maintain the distinctiveness of the task-specific final layers while unifying the initial layers, which are primarily associated with feature extraction. This approach ensures parameter consistency across all layers, essential for boosting performance. Moreover, it facilitates seamless integration of knowledge, enabling effective merging of models from different datasets and tasks. Specifically, we investigate its applicability in unsupervised domain adaptation (UDA), an unexplored area for model merging, for semantic and panoptic segmentation. Experimental results demonstrate substantial UDA improvements without additional costs for merging same-architecture models from distinct datasets (<jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$\\uparrow 2.6\\%$$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mo>\u2191<\/mml:mo>\n                    <mml:mn>2.6<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula> mIoU) and different-architecture models with a shared backbone (<jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$\\uparrow 6.8\\%$$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mo>\u2191<\/mml:mo>\n                    <mml:mn>6.8<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula> mIoU). Furthermore, merging semantic and panoptic segmentation models increases mPQ by 7%. These findings are validated across a wide variety of UDA strategies, architectures and datasets. The code will be publicly available upon acceptance in the LWMM repository: <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/www-vpu.eps.uam.es\/LWMM\/\" ext-link-type=\"uri\">http:\/\/www-vpu.eps.uam.es\/LWMM\/<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s00371-025-03843-7","type":"journal-article","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T23:03:32Z","timestamp":1741043012000},"page":"7867-7882","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Layer-wise model merging for unsupervised domain adaptation in segmentation tasks"],"prefix":"10.1007","volume":"41","author":[{"given":"Roberto","family":"Alcover-Couso","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan C.","family":"SanMiguel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcos","family":"Escudero-Vi\u00f1olo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 M.","family":"Mart\u00ednez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"key":"3843_CR1","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. 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