{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/test-time-domain-generalization-via-universe","title":"Test-Time Domain Generalization via Universe Learning: A Multi-Graph Matching Approach for Medical Image Segmentation","arxiv_id":"2503.13012","date":"2025-03-17","proceeding":"CVPR 2025 1","authors":["Xingguo Lv","Xingbo Dong","LiWen Wang","Jiewen Yang","Lei Zhao","Bin Pu","Zhe Jin","Xuejun Li"],"abstract":"Despite domain generalization (DG) has significantly addressed the performance degradation of pre-trained models caused by domain shifts, it often falls short in real-world deployment. Test-time adaptation (TTA), which adjusts a learned model using unlabeled test data, presents a promising solution. However, most existing TTA methods struggle to deliver strong performance in medical image segmentation, primarily because they overlook the crucial prior knowledge inherent to medical images. To address this challenge, we incorporate morphological information and propose a framework based on multi-graph matching. Specifically, we introduce learnable universe embeddings that integrate morphological priors during multi-source training, along with novel unsupervised test-time paradigms for domain adaptation. This approach guarantees cycle-consistency in multi-matching while enabling the model to more effectively capture the invariant priors of unseen data, significantly mitigating the effects of domain shifts. Extensive experiments demonstrate that our method outperforms other state-of-the-art approaches on two medical image segmentation benchmarks for both multi-source and single-source domain generalization tasks. The source code is available at https://github.com/Yore0/TTDG-MGM.","url_abs":"https://arxiv.org/abs/2503.13012v1","url_pdf":"https://arxiv.org/pdf/2503.13012v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"test-time-domain-generalization-via-universe","repo_url":"https://github.com/yore0/ttdg-mgm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"domain-generalization","task_name":"Domain Generalization"},{"task_slug":"graph-matching","task_name":"Graph Matching"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"single-source-domain-generalization","task_name":"Single-Source Domain Generalization"},{"task_slug":"test-time-adaptation","task_name":"Test-time Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2503.13012","atlas_url":"https://app.syntology.ai/?focus=2503.13012","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.13012"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/Yore0/TTDG-MGM","reach":null}],"summary":{"ran":4,"unverified":1},"by_repo_kind":{"official":{"samples":5,"ran":4,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":5,"samples":[{"code_sha256_prefix":"67772f807f867d4f","entry":"Affinity","repo":"Yore0/TTDG-MGM","repo_kind":"official","path":"adapteacher/modeling/GModule/multi_graph_matching.py","file_url":"https://github.com/Yore0/TTDG-MGM/blob/HEAD/adapteacher/modeling/GModule/multi_graph_matching.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"67772f807f867d4f"}},{"code_sha256_prefix":"203b586120117154","entry":"Feat2Graph","repo":"Yore0/TTDG-MGM","repo_kind":"official","path":"adapteacher/modeling/GModule/multi_graph_matching.py","file_url":"https://github.com/Yore0/TTDG-MGM/blob/HEAD/adapteacher/modeling/GModule/multi_graph_matching.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"203b586120117154"}},{"code_sha256_prefix":"379e1862072096af","entry":"MultiHeadAttention","repo":"Yore0/TTDG-MGM","repo_kind":"official","path":"adapteacher/modeling/GModule/multi_graph_matching.py","file_url":"https://github.com/Yore0/TTDG-MGM/blob/HEAD/adapteacher/modeling/GModule/multi_graph_matching.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"379e1862072096af"}},{"code_sha256_prefix":"e65af3d8e3ea20f6","entry":"dot_attention","repo":"Yore0/TTDG-MGM","repo_kind":"official","path":"adapteacher/modeling/GModule/multi_graph_matching.py","file_url":"https://github.com/Yore0/TTDG-MGM/blob/HEAD/adapteacher/modeling/GModule/multi_graph_matching.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"e65af3d8e3ea20f6"}},{"code_sha256_prefix":"8dfdb0209bb58f77","entry":"G_Universe","repo":"Yore0/TTDG-MGM","repo_kind":"official","path":"adapteacher/modeling/GModule/multi_graph_matching.py","file_url":"https://github.com/Yore0/TTDG-MGM/blob/HEAD/adapteacher/modeling/GModule/multi_graph_matching.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"8dfdb0209bb58f77"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}