{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T23:18:49Z","timestamp":1773357529560,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":18,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,8,6]],"date-time":"2023-08-06T00:00:00Z","timestamp":1691280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,6]]},"DOI":"10.1145\/3599957.3606219","type":"proceedings-article","created":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T17:48:17Z","timestamp":1693331297000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Weakly-supervised Semantic Segmentation on Historical Document Images"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9876-4388","authenticated-orcid":false,"given":"Chulwoo","family":"Pack","sequence":"first","affiliation":[{"name":"South Dakota State University, Brookings, South Dakota"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7476-747X","authenticated-orcid":false,"given":"Dongyoun","family":"Kim","sequence":"additional","affiliation":[{"name":"Iowa State University, Ames, Iowa"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8,29]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00231"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00523"},{"key":"e_1_3_2_1_3_1","volume-title":"Proceedings, Part VII 14","author":"Bearman Amy","year":"2016","unstructured":"Amy Bearman , Olga Russakovsky , Vittorio Ferrari , and Li Fei-Fei . 2016 . What's the point: Semantic segmentation with point supervision. In Computer Vision--ECCV2016: 14thEuropean Conference, Amsterdam, The Netherlands, October 11--14, 2016 , Proceedings, Part VII 14 . Springer, 549--565. Amy Bearman, Olga Russakovsky, Vittorio Ferrari, and Li Fei-Fei. 2016. What's the point: Semantic segmentation with point supervision. In Computer Vision--ECCV2016: 14thEuropean Conference, Amsterdam, The Netherlands, October 11--14, 2016, Proceedings, Part VII 14. Springer, 549--565."},{"key":"e_1_3_2_1_4_1","unstructured":"Christopher Michael Bishop. 2016. Pattern Recognition and Machine Learn- ing. springer.  Christopher Michael Bishop. 2016. Pattern Recognition and Machine Learn- ing. springer."},{"key":"e_1_3_2_1_5_1","volume-title":"The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC genomics 21, 1","author":"Chicco Davide","year":"2020","unstructured":"Davide Chicco and Giuseppe Jurman . 2020. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC genomics 21, 1 ( 2020 ), 1--13. Davide Chicco and Giuseppe Jurman. 2020. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC genomics 21, 1 (2020), 1--13."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.191"},{"key":"e_1_3_2_1_7_1","volume-title":"Adam: A Method for Stochastic Optimization. International Conference on Learning Representations (12","author":"Kingma Diederik","year":"2014","unstructured":"Diederik Kingma and Jimmy Ba . 2014 . Adam: A Method for Stochastic Optimization. International Conference on Learning Representations (12 2014). Diederik Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. International Conference on Learning Representations (12 2014)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1006\/cviu.1998.0684"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412571"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.344"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICFHR-2018.2018.00011"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JEI.30.6.063028"},{"key":"e_1_3_2_1_13_1","volume-title":"U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015: 18th International Conference","author":"Ronneberger Olaf","year":"2015","unstructured":"Olaf Ronneberger , Philipp Fischer , and Thomas Brox . 2015 . U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015: 18th International Conference , Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer , 234--241. Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer, 234--241."},{"key":"e_1_3_2_1_14_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman . 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 ( 2014 ). Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/DAS.2018.39"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2017.94"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"e_1_3_2_1_18_1","article-title":"A brief introduction to weakly supervised learning","volume":"5","author":"Zhou Zhi-Hua","year":"2017","unstructured":"Zhi-Hua Zhou . 2017 . A brief introduction to weakly supervised learning . National Science Review 5 , 1 (08 2017), 44--53. https:\/\/doi.org\/10.1093\/nsr\/nwx106arXiv:https:\/\/academic.oup.com\/nsr\/article-pdf\/5\/1\/44\/31567770\/nwx106.pdf 10.1093\/nsr Zhi-Hua Zhou. 2017. A brief introduction to weakly supervised learning. National Science Review 5, 1 (08 2017), 44--53. https:\/\/doi.org\/10.1093\/nsr\/nwx106arXiv:https:\/\/academic.oup.com\/nsr\/article-pdf\/5\/1\/44\/31567770\/nwx106.pdf","journal-title":"National Science Review"}],"event":{"name":"RACS '23: International Conference on Research in Adaptive and Convergent Systems","location":"Gdansk Poland","acronym":"RACS '23","sponsor":["SIGAPP ACM Special Interest Group on Applied Computing"]},"container-title":["Proceedings of the International Conference on Research in Adaptive and Convergent Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3599957.3606219","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3599957.3606219","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:47:13Z","timestamp":1750178833000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3599957.3606219"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,6]]},"references-count":18,"alternative-id":["10.1145\/3599957.3606219","10.1145\/3599957"],"URL":"https:\/\/doi.org\/10.1145\/3599957.3606219","relation":{},"subject":[],"published":{"date-parts":[[2023,8,6]]},"assertion":[{"value":"2023-08-29","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}