{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:23:35Z","timestamp":1783610615603,"version":"3.55.0"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031198267","type":"print"},{"value":"9783031198274","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-19827-4_20","type":"book-chapter","created":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T14:42:19Z","timestamp":1667313739000},"page":"334-350","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Unknown-Oriented Learning for\u00a0Open Set Domain Adaptation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1327-1315","authenticated-orcid":false,"given":"Jie","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9476-521X","authenticated-orcid":false,"given":"Xiaoqing","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0853-6948","authenticated-orcid":false,"given":"Yixuan","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,2]]},"reference":[{"key":"20_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1007\/978-3-030-58517-4_25","volume-title":"Computer Vision \u2013 ECCV 2020","author":"S Bucci","year":"2020","unstructured":"Bucci, S., Loghmani, M.R., Tommasi, T.: On the effectiveness of image rotation for open set domain adaptation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12361, pp. 422\u2013438. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58517-4_25"},{"key":"20_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1007\/978-3-319-46478-7_25","volume-title":"Computer Vision \u2013 ECCV 2016","author":"S Chandra","year":"2016","unstructured":"Chandra, S., Kokkinos, I.: Fast, exact and multi-scale inference for semantic image segmentation with deep Gaussian CRFs. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 402\u2013418. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_25"},{"issue":"5","key":"20_CR3","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.1109\/TMI.2021.3055290","volume":"40","author":"Z Chen","year":"2021","unstructured":"Chen, Z., Guo, X., Woo, P.Y., Yuan, Y.: Super-resolution enhanced medical image diagnosis with sample affinity interaction. IEEE Trans. Med. Imaging 40(5), 1377\u20131389 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Choi, S., Kim, J.T., Choo, J.: Cars can\u2019t fly up in the sky: improving urban-scene segmentation via height-driven attention networks. In: CVPR, pp. 9373\u20139383 (2020)","DOI":"10.1109\/CVPR42600.2020.00939"},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Cui, S., Wang, S., Zhuo, J., Su, C., Huang, Q., Tian, Q.: Gradually vanishing bridge for adversarial domain adaptation. In: CVPR, pp. 12455\u201312464 (2020)","DOI":"10.1109\/CVPR42600.2020.01247"},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"Dray, X., et al.: Cad-cap: une base de donn\u00e9es fran\u00e7aise \u00e0 vocation internationale, pour le d\u00e9veloppement et la validation d\u2019outils de diagnostic assist\u00e9 par ordinateur en vid\u00e9ocapsule endoscopique du gr\u00eale. Endoscopy 50(03), 000441 (2018)","DOI":"10.1055\/s-0038-1623358"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Du, L., et al.: SSF-DAN: separated semantic feature based domain adaptation network for semantic segmentation. In: CVPR, pp. 982\u2013991 (2019)","DOI":"10.1109\/ICCV.2019.00107"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Feng, Q., Kang, G., Fan, H., Yang, Y.: Attract or distract: exploit the margin of open set. In: ICCV, pp. 7990\u20137999 (2019)","DOI":"10.1109\/ICCV.2019.00808"},{"key":"20_CR9","unstructured":"Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: ICML, pp. 1180\u20131189. PMLR (2015)"},{"issue":"1","key":"20_CR10","first-page":"2030","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2030\u20132096 (2016)","journal-title":"J. Mach. Learn. Res."},{"issue":"4","key":"20_CR11","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1109\/TMI.2020.3046843","volume":"40","author":"X Guo","year":"2020","unstructured":"Guo, X., Yang, C., Liu, Y., Yuan, Y.: Learn to threshold: thresholdnet with confidence-guided manifold mixup for polyp segmentation. IEEE Trans. Med. Imaging 40(4), 1134\u20131146 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"20_CR12","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1109\/34.291440","volume":"16","author":"JJ Hull","year":"1994","unstructured":"Hull, J.J.: A database for handwritten text recognition research. IEEE TPAMI 16(5), 550\u2013554 (1994)","journal-title":"IEEE TPAMI"},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Iscen, A., Tolias, G., Avrithis, Y., Furon, T., Chum, O.: Efficient diffusion on region manifolds: Recovering small objects with compact CNN representations. In: CVPR, pp. 2077\u20132086 (2017)","DOI":"10.1109\/CVPR.2017.105"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Jing, M., Li, J., Zhu, L., Ding, Z., Lu, K., Yang, Y.: Balanced open set domain adaptation via centroid alignment. In: AAAI, vol. 35, pp. 8013\u20138020 (2021)","DOI":"10.1609\/aaai.v35i9.16977"},{"key":"20_CR15","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1109\/TBDATA.2019.2921572","volume":"7","author":"J Johnson","year":"2019","unstructured":"Johnson, J., Douze, M., J\u00e9gou, H.: Billion-scale similarity search with GPUs. IEEE Trans, Big Data. 7, 535\u2013543 (2019)","journal-title":"IEEE Trans, Big Data."},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Kang, G., Jiang, L., Yang, Y., Hauptmann, A.G.: Contrastive adaptation network for unsupervised domain adaptation. In: CVPR, pp. 4893\u20134902 (2019)","DOI":"10.1109\/CVPR.2019.00503"},{"issue":"06","key":"20_CR17","doi-asserted-by":"publisher","first-page":"E477","DOI":"10.1055\/s-0043-105488","volume":"5","author":"A Koulaouzidis","year":"2017","unstructured":"Koulaouzidis, A., et al.: Kid project: an internet-based digital video atlas of capsule endoscopy for research purposes. Endosc. Int. Open 5(06), E477\u2013E483 (2017)","journal-title":"Endosc. Int. Open"},{"key":"20_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1007\/978-3-030-58589-1_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"G Kwon","year":"2020","unstructured":"Kwon, G., Prabhushankar, M., Temel, D., AlRegib, G.: Backpropagated gradient representations for anomaly detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12366, pp. 206\u2013226. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58589-1_13"},{"issue":"11","key":"20_CR19","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"20_CR20","doi-asserted-by":"crossref","unstructured":"Lee, C.Y., Batra, T., Baig, M.H., Ulbricht, D.: Sliced Wasserstein discrepancy for unsupervised domain adaptation. In: CVPR, pp. 10285\u201310295 (2019)","DOI":"10.1109\/CVPR.2019.01053"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Li, J., Chen, E., Ding, Z., Zhu, L., Lu, K., Huang, Z.: Cycle-consistent conditional adversarial transfer networks. In: ACM MM, pp. 747\u2013755 (2019)","DOI":"10.1145\/3343031.3350902"},{"key":"20_CR22","doi-asserted-by":"publisher","first-page":"3918","DOI":"10.1109\/TPAMI.2020.2991050","volume":"43","author":"J Li","year":"2020","unstructured":"Li, J., Chen, E., Ding, Z., Zhu, L., Lu, K., Shen, H.T.: Maximum density divergence for domain adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 43, 3918\u20133930 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"20_CR23","doi-asserted-by":"crossref","unstructured":"Liu, H., Cao, Z., Long, M., Wang, J., Yang, Q.: Separate to adapt: open set domain adaptation via progressive separation. In: CVPR, pp. 2927\u20132936 (2019)","DOI":"10.1109\/CVPR.2019.00304"},{"key":"20_CR24","doi-asserted-by":"crossref","unstructured":"Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: SphereFace: deep hypersphere embedding for face recognition. In: CVPR, pp. 212\u2013220 (2017)","DOI":"10.1109\/CVPR.2017.713"},{"key":"20_CR25","unstructured":"Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: ICML, pp. 97\u2013105. PMLR (2015)"},{"key":"20_CR26","unstructured":"Long, M., Zhu, H., Wang, J., Jordan, M.I.: Unsupervised domain adaptation with residual transfer networks. In: NIPS (2016)"},{"key":"20_CR27","unstructured":"Long, M., Zhu, H., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: ICML, pp. 2208\u20132217. PMLR (2017)"},{"key":"20_CR28","unstructured":"Luo, Y., Wang, Z., Huang, Z., Baktashmotlagh, M.: Progressive graph learning for open-set domain adaptation. In: ICML, pp. 6468\u20136478. PMLR (2020)"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Merrill, N., Olson, C.C.: Unsupervised ensemble-kernel principal component analysis for hyperspectral anomaly detection. In: CVPR, pp. 112\u2013113 (2020)","DOI":"10.1109\/CVPRW50498.2020.00064"},{"key":"20_CR30","unstructured":"Mu, F., Liang, Y., Li, Y.: Gradients as features for deep representation learning. In: ICLR (2020). https:\/\/openreview.net\/forum?id=BkeoaeHKDS"},{"key":"20_CR31","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning (2011)"},{"key":"20_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2017.05.026","volume":"133","author":"C O\u2019Reilly","year":"2017","unstructured":"O\u2019Reilly, C., Moessner, K., Nati, M.: Univariate and multivariate time series manifold learning. Knowl. Based Syst. 133, 1\u201316 (2017)","journal-title":"Knowl. Based Syst."},{"key":"20_CR33","doi-asserted-by":"crossref","unstructured":"Pan, Y., Yao, T., Li, Y., Ngo, C.W., Mei, T.: Exploring category-agnostic clusters for open-set domain adaptation. In: CVPR, pp. 13867\u201313875 (2020)","DOI":"10.1109\/CVPR42600.2020.01388"},{"key":"20_CR34","doi-asserted-by":"crossref","unstructured":"Panareda Busto, P., Gall, J.: Open set domain adaptation. In: ICCV, pp. 754\u2013763 (2017)","DOI":"10.1109\/ICCV.2017.88"},{"key":"20_CR35","doi-asserted-by":"crossref","unstructured":"Saito, K., Saenko, K.: Ovanet: One-vs-all network for universal domain adaptation. In: ICCV, pp. 9000\u20139009 (2021)","DOI":"10.1109\/ICCV48922.2021.00887"},{"key":"20_CR36","doi-asserted-by":"crossref","unstructured":"Saito, K., Watanabe, K., Ushiku, Y., Harada, T.: Maximum classifier discrepancy for unsupervised domain adaptation. In: CVPR, pp. 3723\u20133732 (2018)","DOI":"10.1109\/CVPR.2018.00392"},{"key":"20_CR37","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1007\/978-3-030-01228-1_10","volume-title":"Computer Vision \u2013 ECCV 2018","author":"K Saito","year":"2018","unstructured":"Saito, K., Yamamoto, S., Ushiku, Y., Harada, T.: Open set domain adaptation by backpropagation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 156\u2013171. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01228-1_10"},{"key":"20_CR38","doi-asserted-by":"crossref","unstructured":"Tang, H., Chen, K., Jia, K.: Unsupervised domain adaptation via structurally regularized deep clustering. In: CVPR, pp. 8725\u20138735 (2020)","DOI":"10.1109\/CVPR42600.2020.00875"},{"key":"20_CR39","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: CVPR, pp. 7167\u20137176 (2017)","DOI":"10.1109\/CVPR.2017.316"},{"key":"20_CR40","doi-asserted-by":"crossref","unstructured":"VS, V., Gupta, V., Oza, P., Sindagi, V.A., Patel, V.M.: Mega-CDA: memory guided attention for category-aware unsupervised domain adaptive object detection. In: CVPR, pp. 4516\u20134526 (2021)","DOI":"10.1109\/CVPR46437.2021.00449"},{"key":"20_CR41","doi-asserted-by":"crossref","unstructured":"Wang, X., Li, L., Ye, W., Long, M., Wang, J.: Transferable attention for domain adaptation. In: AAAI, vol. 33, pp. 5345\u20135352 (2019)","DOI":"10.1609\/aaai.v33i01.33015345"},{"key":"20_CR42","unstructured":"Xie, S., Zheng, Z., Chen, L., Chen, C.: Learning semantic representations for unsupervised domain adaptation. In: ICML, pp. 5423\u20135432. PMLR (2018)"},{"key":"20_CR43","doi-asserted-by":"crossref","unstructured":"Xu, R., et al.: Joint partial optimal transport for open set domain adaptation. In: IJCAI, pp. 2540\u20132546 (2020)","DOI":"10.24963\/ijcai.2020\/352"},{"key":"20_CR44","unstructured":"Zellinger, W., Grubinger, T., Lughofer, E., Natschl\u00e4ger, T., Saminger-Platz, S.: Central moment discrepancy (CMD) for domain-invariant representation learning. arXiv preprint arXiv:1702.08811 (2017)"},{"key":"20_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tang, H., Jia, K., Tan, M.: Domain-symmetric networks for adversarial domain adaptation. In: CVPR, pp. 5031\u20135040 (2019)","DOI":"10.1109\/CVPR.2019.00517"},{"key":"20_CR46","doi-asserted-by":"crossref","unstructured":"Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: CVPR, pp. 6881\u20136890 (2021)","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"20_CR47","doi-asserted-by":"crossref","unstructured":"Zhong, L., Fang, Z., Liu, F., Yuan, B., Zhang, G., Lu, J.: Bridging the theoretical bound and deep algorithms for open set domain adaptation. IEEE Trans. Neural Netw. Learn. (2021)","DOI":"10.1109\/TNNLS.2021.3119965"},{"key":"20_CR48","unstructured":"Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Sch\u00f6lkopf, B.: Learning with local and global consistency. In: NeurIPS, pp. 321\u2013328 (2004)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19827-4_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T14:48:10Z","timestamp":1667314090000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19827-4_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198267","9783031198274"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19827-4_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"2 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"0","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":"28% - 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":"3.21","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":"3.91","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)"}}]}}