{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:06:12Z","timestamp":1750309572224,"version":"3.41.0"},"reference-count":50,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T00:00:00Z","timestamp":1739836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62176017, 82441024, 62302031"],"award-info":[{"award-number":["62176017, 82441024, 62302031"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LQ23F020024"],"award-info":[{"award-number":["LQ23F020024"]}]},{"name":"\u201cPioneer\u201d and \u201cLeading Goose\u201d R&D Program of Zhejiang","award":["2024C01020"],"award-info":[{"award-number":["2024C01020"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>\n            Learning open-vocabulary semantic segmentation (OVSS) from text supervision has recently received increasing attention for its promising potential in real-world applications. However, only with image-level supervision, it struggles to achieve dense and robust cross-modal alignment and thus limits pixel-level predictions. In this article, we present a novel approach to this task with\n            <jats:italic>M<\/jats:italic>\n            ulti-\n            <jats:italic>G<\/jats:italic>\n            rained\n            <jats:italic>C<\/jats:italic>\n            ross-modal\n            <jats:italic>C<\/jats:italic>\n            ontrastive\n            <jats:italic>L<\/jats:italic>\n            earning, named MGCCL. Specifically, unlike current solutions restricted by coarse image\/object-text alignment, MGCCL constructs pseudo multi-granular semantic correspondences at the object-, part-, and pixel-level and collaborates with hard sampling strategies to conduct cross-modal contrastive learning, significantly facilitating fine-grained alignment. Further, we develop an adaptive semantic unit which flexibly harnesses the learned multi-grained cross-modal alignment capabilities to effectively mitigate the under- and over-segmentation issues arising from the per-group and per-pixel units. Extensive experiments over a broad suite of eight segmentation benchmarks show that our approach delivers significant advancements over state-of-the-art counterparts, demonstrating its effectiveness.\n          <\/jats:p>","DOI":"10.1145\/3711868","type":"journal-article","created":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T14:38:01Z","timestamp":1736519881000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Grained Contrastive Learning for Text-Supervised Open-Vocabulary Semantic Segmentation"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0879-5894","authenticated-orcid":false,"given":"Yajie","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Complex and Critical Software Environment and School of Computer Science and Engineering, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4444-3159","authenticated-orcid":false,"given":"Pu","family":"Ge","sequence":"additional","affiliation":[{"name":"Hangzhou Innovation Institute, Beihang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8642-396X","authenticated-orcid":false,"given":"Guodong","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Complex and Critical Software Environment and School of Computer Science and Engineering, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5181-6451","authenticated-orcid":false,"given":"Qingjie","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2412-9330","authenticated-orcid":false,"given":"Di","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Complex and Critical Software Environment and School of Computer Science and Engineering, Beihang University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,2,18]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"4253","volume-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Araslanov Nikita","year":"2020","unstructured":"Nikita Araslanov and Stefan Roth. 2020. 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