{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T06:32:07Z","timestamp":1743057127408,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030863647"},{"type":"electronic","value":"9783030863654"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-86365-4_38","type":"book-chapter","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T11:02:39Z","timestamp":1631271759000},"page":"471-482","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Weakly Supervised Semantic Segmentation with Patch-Based Metric Learning Enhancement"],"prefix":"10.1007","author":[{"given":"Patrick P. K.","family":"Chan","sequence":"first","affiliation":[]},{"given":"Keke","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Linyi","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Xiaoman","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Daniel S.","family":"Yeung","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,7]]},"reference":[{"key":"38_CR1","doi-asserted-by":"crossref","unstructured":"Dai, J.F., He, K.M., Sun, J.: BoxSup: exploiting bounding boxes to supervise convolutional networks for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1635\u20131643. IEEE (2015)","DOI":"10.1109\/ICCV.2015.191"},{"key":"38_CR2","doi-asserted-by":"crossref","unstructured":"Lin, D., Dai, J.F., et al.: ScribbleSup: scribble-supervised convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3159\u20133167. IEEE (2017)","DOI":"10.1109\/CVPR.2016.344"},{"key":"38_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1007\/978-3-319-46493-0_42","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Kolesnikov","year":"2016","unstructured":"Kolesnikov, A., Lampert, C.H.: Seed, expand and constrain: three principles for weakly-supervised image segmentation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 695\u2013711. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_42"},{"key":"38_CR4","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.F.: ImageNet: a large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"38_CR5","doi-asserted-by":"crossref","unstructured":"Zhou, B.L., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929. IEEE (2016)","DOI":"10.1109\/CVPR.2016.319"},{"key":"38_CR6","doi-asserted-by":"crossref","unstructured":"Wu, C., Manmatha, R., Smola, A.J., Krahenbuhl, P.: Sampling matters in deep embedding learning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2859\u20132867. IEEE (2017)","DOI":"10.1109\/ICCV.2017.309"},{"key":"38_CR7","doi-asserted-by":"crossref","unstructured":"Wei, Y.C., Feng, J.S., Liang, X.D., Cheng, M.M., Zhao, Y., Yan, S.C: Object region mining with adversarial erasing: a simple classification to semantic segmentation approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6488\u20136496. IEEE (2017)","DOI":"10.1109\/CVPR.2017.687"},{"key":"38_CR8","doi-asserted-by":"crossref","unstructured":"Lee, J., Kim, E., et al.: FickleNet: weakly and semi-supervised semantic image segmentation using stochastic inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5267\u20135276. IEEE (2019)","DOI":"10.1109\/CVPR.2019.00541"},{"key":"38_CR9","doi-asserted-by":"crossref","unstructured":"Wang, Y.D., Zhang, J., Kan, M.N., Shan, S.G., Chen, X.L.: Self-supervised scale equivariant network for weakly supervised semantic segmentation. arXiv: Computer Vision and Pattern Recognition (2019)","DOI":"10.1109\/CVPR42600.2020.01229"},{"key":"38_CR10","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, J., Kan, M., Shan, S., Chen, X.: Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12272\u201312281. IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.01229"},{"key":"38_CR11","doi-asserted-by":"crossref","unstructured":"Chang, Y.T., Wang, Q.S., Hung, W.C., Robinson, P.: Weakly-supervised semantic segmentation via sub-category exploration. In: Proceedings of the International Conference on Computer Vision, IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00901"},{"key":"38_CR12","doi-asserted-by":"crossref","unstructured":"Ahn, J., Kwak, S.: Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4981\u20134990. IEEE (2018)","DOI":"10.1109\/CVPR.2018.00523"},{"key":"38_CR13","doi-asserted-by":"crossref","unstructured":"Ahn, J., Cho, S., Kwak, S.: Weakly supervised learning of instance segmentation with inter-pixel relations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2204\u20132213. IEEE (2019)","DOI":"10.1109\/CVPR.2019.00231"},{"key":"38_CR14","doi-asserted-by":"crossref","unstructured":"Huang, Z.L., Wang, X.G., Wang, J.S., Liu, W.Y., Wang, J.D.: Weakly-supervised semantic segmentation network with deep seeded region growing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7014\u20137023. IEEE (2018)","DOI":"10.1109\/CVPR.2018.00733"},{"key":"38_CR15","doi-asserted-by":"crossref","unstructured":"Fan, J.S., Zhang, Z.X., Tan, T.N., Song, C.F., Xiao, J.: CIAN: cross-image affinity net for weakly supervised semantic segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 10762\u201310769 (2020)","DOI":"10.1609\/aaai.v34i07.6705"},{"key":"38_CR16","doi-asserted-by":"crossref","unstructured":"Fan, J., Zhang, Z., Song, C., Tan, T.: Learning integral objects with intra-class discriminator for weakly-supervised semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4282\u20134291. IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00434"},{"key":"38_CR17","doi-asserted-by":"crossref","unstructured":"Zhang, D., Zhang, H.W., Tang, J.H., Hua, X.S., Sun, Q.R.: Causal intervention for weakly-supervised semantic segmentation. In: Proceedings of the Conference on Neural Information Processing Systems (2020)","DOI":"10.1109\/ICIP40778.2020.9190911"},{"key":"38_CR18","doi-asserted-by":"crossref","unstructured":"Boiarov, A., Tyantov, E.: Large scale landmark recognition via deep metric learning. In: Proceedings of the ACM International Conference, pp. 169\u2013178 (2019)","DOI":"10.1145\/3357384.3357956"},{"key":"38_CR19","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815\u2013823. IEEE (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"38_CR20","doi-asserted-by":"crossref","unstructured":"Neubeck, A., Van Gool, L.: Efficient non-maximum suppression. In: Proceedings of the International Conference on Pattern Recognition, pp. 850\u2013855. IEEE (2006)","DOI":"10.1109\/ICPR.2006.479"},{"issue":"1","key":"38_CR21","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"M Everingham","year":"2015","unstructured":"Everingham, M., Eslami, S.M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. Int. J. Comput. Vision 111(1), 98\u2013136 (2015)","journal-title":"Int. J. Comput. Vision"},{"issue":"2","key":"38_CR22","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vision 88(2), 303\u2013338 (2010)","journal-title":"Int. J. Comput. Vision"},{"key":"38_CR23","doi-asserted-by":"crossref","unstructured":"Jiang, P.T., Hou, Q.B., Cao, Y., Cheng, M.M., Wei, Y.C., Xiong, H.K.: Integral object mining via online attention accumulation. In: Proceedings of the International Conference on Computer Vision. IEEE (2019)","DOI":"10.1109\/ICCV.2019.00216"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86365-4_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T11:14:00Z","timestamp":1631272440000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86365-4_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030863647","9783030863654"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86365-4_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bratislava","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovakia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"496","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"265","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"53% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference was held online due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}