{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:05:10Z","timestamp":1750309510336,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["61976217"],"award-info":[{"award-number":["61976217"]}]},{"name":"the National Natural Science Foundation of China","award":["62306320"],"award-info":[{"award-number":["62306320"]}]},{"name":"the Natural Science Foundation of Jiangsu Province","award":["BK20231063"],"award-info":[{"award-number":["BK20231063"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,28]]},"DOI":"10.1145\/3664647.3680627","type":"proceedings-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T06:59:27Z","timestamp":1729925967000},"page":"7220-7228","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning from Concealed Labels"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3364-8703","authenticated-orcid":false,"given":"Zhongnian","family":"Li","sequence":"first","affiliation":[{"name":"China University of Mining Technology, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3836-6487","authenticated-orcid":false,"given":"Meng","family":"Wei","sequence":"additional","affiliation":[{"name":"China University of Mining Technology, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8007-5583","authenticated-orcid":false,"given":"Peng","family":"Ying","sequence":"additional","affiliation":[{"name":"China University of Mining Technology, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8344-2597","authenticated-orcid":false,"given":"Tongfeng","family":"Sun","sequence":"additional","affiliation":[{"name":"China University of Mining Technology, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6973-799X","authenticated-orcid":false,"given":"Xinzheng","family":"Xu","sequence":"additional","affiliation":[{"name":"China University of Mining Technology, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01519"},{"key":"e_1_3_2_1_2_1","volume-title":"International Conference on Machine Learning, ICML","volume":"80","author":"Bao Han","year":"2018","unstructured":"Han Bao, Gang Niu, and Masashi Sugiyama. 2018. Classification from Pairwise Similarity and Unlabeled Data. In International Conference on Machine Learning, ICML 2018, Stockholmsm\u00e4ssan, Stockholm, Sweden, Vol. 80. 461--470."},{"key":"e_1_3_2_1_3_1","volume-title":"Submodular Feature Selection for Partial Label Learning. In The SIGKDD Conference on Knowledge Discovery and Data Mining, KDD","author":"Bao Wei-Xuan","year":"2022","unstructured":"Wei-Xuan Bao, Jun-Yi Hang, and Min-Ling Zhang. 2022. Submodular Feature Selection for Partial Label Learning. In The SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022, Washington, DC. ACM, 26--34."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-020-05877-5"},{"key":"e_1_3_2_1_5_1","volume-title":"Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD","volume":"11907","author":"Bekker Jessa","year":"2019","unstructured":"Jessa Bekker, Pieter Robberechts, and Jesse Davis. 2019. Beyond the Selected Completely at Random Assumption for Learning from Positive and Unlabeled Data. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, W\u00fcrzburg, Germany, Vol. 11907. 71--85."},{"key":"e_1_3_2_1_6_1","volume-title":"Annual Conference on Neural Information Processing Systems, NeurIPS 2023","author":"Brahmbhatt Anand","year":"2023","unstructured":"Anand Brahmbhatt, Rishi Saket, and Aravindan Raghuveer. 2023. PAC Learning Linear Thresholds from Label Proportions. Annual Conference on Neural Information Processing Systems, NeurIPS 2023, New Orleans, LA, USA 36 (2023)."},{"key":"e_1_3_2_1_7_1","volume-title":"Proceedings of the International Conference on Machine Learning, ICML","volume":"139","author":"Cao Yuzhou","year":"2021","unstructured":"Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, and Masashi Sugiyama. 2021. Learning from Similarity-Confidence Data. In Proceedings of the International Conference on Machine Learning, ICML 2021, Virtual Event, Vol. 139. 1272--1282."},{"key":"e_1_3_2_1_8_1","volume-title":"Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessment. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR","author":"Cao Yue","year":"2022","unstructured":"Yue Cao, Zhaolin Wan, Dongwei Ren, Zifei Yan, and Wangmeng Zuo. 2022. Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessment. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA. IEEE, 5841--5851."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3071924"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86520-7_15"},{"key":"e_1_3_2_1_11_1","volume-title":"Annual Conference on Neural Information Processing Systems, NeurIPS","author":"du Plessis Marthinus Christoffel","year":"2014","unstructured":"Marthinus Christoffel du Plessis, Gang Niu, and Masashi Sugiyama. 2014. Analysis of Learning from Positive and Unlabeled Data. In Annual Conference on Neural Information Processing Systems, NeurIPS 2014, Montreal, Quebec, Canada. 703--711."},{"key":"e_1_3_2_1_12_1","volume-title":"International Conference on Machine Learning, ICML","volume":"37","author":"du Plessis Marthinus Christoffel","year":"2015","unstructured":"Marthinus Christoffel du Plessis, Gang Niu, and Masashi Sugiyama. 2015. Convex Formulation for Learning from Positive and Unlabeled Data. In International Conference on Machine Learning, ICML 2015, Lille, France, Vol. 37. 1386--1394."},{"key":"e_1_3_2_1_13_1","volume-title":"Proceedings of the 38th International Conference on Machine Learning, ICML 2021","volume":"139","author":"Gao Yi","year":"2021","unstructured":"Yi Gao and Min-Ling Zhang. 2021. Discriminative Complementary-Label Learning with Weighted Loss. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, Virtual, Vol. 139. 3587--3597."},{"key":"e_1_3_2_1_14_1","volume-title":"Annual Conference on Neural Information Processing Systems, NeurIPS","author":"Ishida Takashi","year":"2017","unstructured":"Takashi Ishida, Gang Niu, Weihua Hu, and Masashi Sugiyama. 2017. Learning from Complementary Labels. In Annual Conference on Neural Information Processing Systems, NeurIPS 2017, Long Beach, CA. 5639--5649."},{"key":"e_1_3_2_1_15_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning, ICML 2019","volume":"97","author":"Ishida Takashi","year":"2019","unstructured":"Takashi Ishida, Gang Niu, Aditya Krishna Menon, and Masashi Sugiyama. 2019. Complementary-Label Learning for Arbitrary Losses and Models. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, Long Beach, California, Vol. 97. 2971--2980."},{"key":"e_1_3_2_1_16_1","volume-title":"Annual Conference on Neural Information Processing Systems, NeurIPS","author":"Ishida Takashi","year":"2018","unstructured":"Takashi Ishida, Gang Niu, and Masashi Sugiyama. 2018. Binary Classification from Positive-Confidence Data. In Annual Conference on Neural Information Processing Systems, NeurIPS 2018, Montr\u00e9al, Canada. 5921--5932."},{"key":"e_1_3_2_1_17_1","volume-title":"OSSGAN: Open-Set Semi-Supervised Image Generation. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR","author":"Katsumata Kai","year":"2022","unstructured":"Kai Katsumata, Duc Minh Vo, and Hideki Nakayama. 2022. OSSGAN: Open-Set Semi-Supervised Image Generation. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA. IEEE, 11175--11183."},{"key":"e_1_3_2_1_18_1","volume-title":"Propagation Regularizer for Semisupervised Learning with Extremely Scarce Labeled Samples. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR","author":"Kim Noo-Ri","year":"2022","unstructured":"Noo-Ri Kim and Jee-Hyong Lee. 2022. Propagation Regularizer for Semisupervised Learning with Extremely Scarce Labeled Samples. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA. IEEE, 14381--14390."},{"key":"e_1_3_2_1_19_1","volume-title":"Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023","author":"Lin Tiancheng","year":"2023","unstructured":"Tiancheng Lin, Zhimiao Yu, Hongyu Hu, Yi Xu, and Chang Wen Chen. 2023. Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada. IEEE, 19830--19839."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01899"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3296156"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3089337"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3149926"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00327"},{"key":"e_1_3_2_1_25_1","volume-title":"International Conference on Learning Representations, ICLR","author":"Lu Nan","year":"2019","unstructured":"Nan Lu, Gang Niu, Aditya Krishna Menon, and Masashi Sugiyama. 2019. On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data. In International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA."},{"key":"e_1_3_2_1_26_1","unstructured":"Mehryar Mohri Afshin Rostamizadeh and Ameet Talwalkar. 2018. Foundations of machine learning."},{"key":"e_1_3_2_1_27_1","volume-title":"Annual Conference on Neural Information Processing Systems, NeurIPS","author":"Niu Gang","year":"2016","unstructured":"Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Yao Ma, and Masashi Sugiyama. 2016. Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning. In Annual Conference on Neural Information Processing Systems, NeurIPS 2016, Barcelona, Spain. 1199--1207."},{"key":"e_1_3_2_1_28_1","volume-title":"Annual Conference on Neural Information Processing Systems, NeurIPS, Montreal, Quebec. 190--198","author":"Patrini Giorgio","year":"2014","unstructured":"Giorgio Patrini, Richard Nock, Tib\u00e9rio S. Caetano, and Paul Rivera. 2014. (Almost) No Label No Cry. In Annual Conference on Neural Information Processing Systems, NeurIPS, Montreal, Quebec. 190--198."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_01373"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00160"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01553"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3120012"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i17.29888"},{"key":"e_1_3_2_1_34_1","volume-title":"Negatives Make A Positive: An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning","author":"Wei Xiu-Shen","year":"2023","unstructured":"Xiu-Shen Wei, He-Yang Xu, Zhiwen Yang, Chen-Long Duan, and Yuxin Peng. 2023. Negatives Make A Positive: An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning. IEEE Trans. Pattern Anal. Mach. Intell. (2023)."},{"key":"e_1_3_2_1_35_1","volume-title":"Proceedings of the 39th International Conference on Machine Learning, ICML 2022","volume":"162","author":"Wu Dong-Dong","year":"2022","unstructured":"Dong-Dong Wu, Deng-Bao Wang, and Min-Ling Zhang. 2022. Revisiting Consistency Regularization for Deep Partial Label Learning. In Proceedings of the 39th International Conference on Machine Learning, ICML 2022, Baltimore, Maryland, Vol. 162. PMLR, 24212--24225."},{"key":"e_1_3_2_1_36_1","first-page":"3047","article-title":"Extended TT: Learning With Mixed Closed-Set and Open-Set Noisy Labels","volume":"45","author":"Xia Xiaobo","year":"2023","unstructured":"Xiaobo Xia, Bo Han, Nannan Wang, Jiankang Deng, Jiatong Li, Yinian Mao, and Tongliang Liu. 2023. Extended TT: Learning With Mixed Closed-Set and Open-Set Noisy Labels. IEEE Trans. Pattern Anal. Mach. Intell. 45, 3 (2023), 3047--3058.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"e_1_3_2_1_37_1","volume-title":"ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning. Annual Conference on Neural Information Processing Systems 2023","author":"Xu Mingyu","year":"2023","unstructured":"Mingyu Xu, Zheng Lian, Lei Feng, Bin Liu, and Jianhua Tao. 2023. ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning. Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA 36 (2023)."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/444"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i15.29562"},{"key":"e_1_3_2_1_40_1","volume-title":"Proceedings of the International Conference on Machine Learning, ICML","volume":"28","author":"Yu Felix X.","year":"2013","unstructured":"Felix X. Yu, Dong Liu, Sanjiv Kumar, Tony Jebara, and Shih-Fu Chang. 2013. SVM for Learning with Label Proportions. In Proceedings of the International Conference on Machine Learning, ICML 2013, Atlanta, GA, Vol. 28. JMLR.org, 504--512."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.5555\/1005332.1044701"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.11"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109984"},{"key":"e_1_3_2_1_44_1","volume-title":"An Unbiased Risk Estimator for Learning with Augmented Classes. In Annual Conference on Neural Information Processing Systems, NeurIPS","author":"Zhang Yu-Jie","year":"2020","unstructured":"Yu-Jie Zhang, Peng Zhao, Lanjihong Ma, and Zhi-Hua Zhou. 2020. An Unbiased Risk Estimator for Learning with Augmented Classes. In Annual Conference on Neural Information Processing Systems, NeurIPS 2020, virtual."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01543"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.498"}],"event":{"name":"MM '24: The 32nd ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Melbourne VIC Australia","acronym":"MM '24"},"container-title":["Proceedings of the 32nd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3680627","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3664647.3680627","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:57Z","timestamp":1750295877000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3680627"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,28]]},"references-count":46,"alternative-id":["10.1145\/3664647.3680627","10.1145\/3664647"],"URL":"https:\/\/doi.org\/10.1145\/3664647.3680627","relation":{},"subject":[],"published":{"date-parts":[[2024,10,28]]},"assertion":[{"value":"2024-10-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}