{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T03:36:51Z","timestamp":1768275411866,"version":"3.49.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T00:00:00Z","timestamp":1664064000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T00:00:00Z","timestamp":1664064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"publisher","award":["cstc2018jcyjAX0532"],"award-info":[{"award-number":["cstc2018jcyjAX0532"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s13042-022-01661-0","type":"journal-article","created":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T10:02:24Z","timestamp":1664100144000},"page":"761-772","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Learning exclusive discriminative semantic information for zero-shot learning"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7531-4341","authenticated-orcid":false,"given":"Jian-Xun","family":"Mi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhonghao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Debao","family":"Tai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li-Fang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,25]]},"reference":[{"issue":"7","key":"1661_CR1","doi-asserted-by":"publisher","first-page":"1425","DOI":"10.1109\/TPAMI.2015.2487986","volume":"38","author":"Z Akata","year":"2015","unstructured":"Akata Z, Perronnin F, Harchaoui Z, Schmid C (2015) Label-embedding for image classification. IEEE Trans Pattern Anal Mach Intell 38(7):1425\u20131438","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"9","key":"1661_CR2","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1145\/361573.361582","volume":"15","author":"RH Bartels","year":"1972","unstructured":"Bartels RH, Stewart GW (1972) Solution of the Matrix Equation AX + XB = C [F4]. Commun ACM 15(9):820\u2013826. https:\/\/doi.org\/10.1145\/361573.361582","journal-title":"Commun ACM"},{"key":"1661_CR3","doi-asserted-by":"crossref","unstructured":"Boyd S, Parikh N, Chu E (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Now Publishers Inc","DOI":"10.1561\/9781601984616"},{"key":"1661_CR4","doi-asserted-by":"crossref","unstructured":"Changpinyo S, Chao W.L, Gong B, Sha F (2016) Synthesized classifiers for zero-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5327\u20135336","DOI":"10.1109\/CVPR.2016.575"},{"key":"1661_CR5","doi-asserted-by":"crossref","unstructured":"Chen L, Zhang H, Xiao J, Liu W, Chang S.F (2018) Zero-shot visual recognition using semantics-preserving adversarial embedding networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1043\u20131052","DOI":"10.1109\/CVPR.2018.00115"},{"key":"1661_CR6","doi-asserted-by":"crossref","unstructured":"Ding Z, Liu H (2019) Marginalized latent semantic encoder for zero-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6191\u20136199","DOI":"10.1109\/CVPR.2019.00635"},{"key":"1661_CR7","doi-asserted-by":"crossref","unstructured":"Farhadi A, Endres I, Hoiem D, Forsyth D (2009) Describing objects by their attributes. In: 2009 IEEE conference on computer vision and pattern recognition, pp. 1778\u20131785. IEEE","DOI":"10.1109\/CVPR.2009.5206772"},{"key":"1661_CR8","unstructured":"Frome A, Corrado G, Shlens J, Bengio S, Dean J, Ranzato M, Mikolov T (2013) Devise: A deep visual-semantic embedding model"},{"issue":"11","key":"1661_CR9","doi-asserted-by":"publisher","first-page":"2332","DOI":"10.1109\/TPAMI.2015.2408354","volume":"37","author":"Y Fu","year":"2015","unstructured":"Fu Y, Hospedales TM, Xiang T, Gong S (2015) Transductive multi-view zero-shot learning. IEEE Trans Pattern Anal Mach Intell 37(11):2332\u20132345","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1661_CR10","doi-asserted-by":"crossref","unstructured":"Guo Y, Din, G, Han J, Yan C, Zhang J, Dai Q (2019) Landmark selection for zero-shot learning. In: IJCAI, pp. 2435\u20132441","DOI":"10.24963\/ijcai.2019\/338"},{"key":"1661_CR11","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1661_CR12","doi-asserted-by":"publisher","first-page":"1958","DOI":"10.1109\/TIP.2019.2947780","volume":"29","author":"Z Jia","year":"2020","unstructured":"Jia Z, Zhang Z, Wang L, Shan C, Tan T (2020) Deep unbiased embedding transfer for zero-shot learning. IEEE Trans Image Process 29:1958\u20131971","journal-title":"IEEE Trans Image Process"},{"key":"1661_CR13","doi-asserted-by":"crossref","unstructured":"Jiang H, Wang R, Shan S, Yang Y, Chen X (2017) Learning discriminative latent attributes for zero-shot classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4223\u20134232","DOI":"10.1109\/ICCV.2017.453"},{"key":"1661_CR14","doi-asserted-by":"crossref","unstructured":"Kodirov E, Xiang T, Gong S (2017) Semantic autoencoder for zero-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3174\u20133183","DOI":"10.1109\/CVPR.2017.473"},{"key":"1661_CR15","doi-asserted-by":"crossref","unstructured":"Lampert C.H, Nickisch H, Harmeling S (2009) Learning to detect unseen object classes by between-class attribute transfer. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 951\u2013958. IEEE","DOI":"10.1109\/CVPR.2009.5206594"},{"key":"1661_CR16","doi-asserted-by":"publisher","first-page":"5817","DOI":"10.1109\/TIP.2020.2986892","volume":"29","author":"J Li","year":"2020","unstructured":"Li J, Lan X, Long Y, Liu Y, Chen X, Shao L, Zheng N (2020) A joint label space for generalized zero-shot classification. IEEE Trans Image Process 29:5817\u20135831","journal-title":"IEEE Trans Image Process"},{"key":"1661_CR17","unstructured":"Liu J, Li X, Yang G (2018) Cross-class sample synthesis for zero-shot learning. In: BMVC, p. 113"},{"key":"1661_CR18","doi-asserted-by":"crossref","unstructured":"Liu L, Zhou T, Long G, Jiang J, Zhang C (2020) Attribute propagation network for graph zero-shot learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 4868\u20134875","DOI":"10.1609\/aaai.v34i04.5923"},{"key":"1661_CR19","doi-asserted-by":"publisher","first-page":"4788","DOI":"10.1109\/TIP.2020.2975980","volume":"29","author":"Y Liu","year":"2020","unstructured":"Liu Y, Tuytelaars T (2020) A deep multi-modal explanation model for zero-shot learning. IEEE Trans Image Process 29:4788\u20134803","journal-title":"IEEE Trans Image Process"},{"key":"1661_CR20","doi-asserted-by":"crossref","unstructured":"Liu Y, Xie D.Y, Gao Q, Han J, Wang S, Gao X (2019) Graph and autoencoder based feature extraction for zero-shot learning. In: IJCAI, pp. 3038\u20133044","DOI":"10.24963\/ijcai.2019\/421"},{"key":"1661_CR21","unstructured":"Van\u00a0der Maaten L, Hinton G (2008) Visualizing data using t-sne. Journal of machine learning research 9(11)"},{"key":"1661_CR22","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality. arXiv preprint arXiv:1310.4546"},{"key":"1661_CR23","doi-asserted-by":"crossref","unstructured":"Ming D, Ding C (2019) Robust flexible feature selection via exclusive l21 regularization. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 3158\u20133164","DOI":"10.24963\/ijcai.2019\/438"},{"key":"1661_CR24","unstructured":"Norouzi M, Mikolov T, Bengio S, Singer Y, Shlens J, Frome A, Corrado G.S, Dean J (2013) Zero-shot learning by convex combination of semantic embeddings. arXiv preprint arXiv:1312.5650"},{"key":"1661_CR25","doi-asserted-by":"crossref","unstructured":"Pambala A, Dutta T, Biswas S (2020) Generative model with semantic embedding and integrated classifier for generalized zero-shot learning. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1237\u20131246","DOI":"10.1109\/WACV45572.2020.9093625"},{"issue":"1\u20132","key":"1661_CR26","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s11263-013-0695-z","volume":"108","author":"G Patterson","year":"2014","unstructured":"Patterson G, Xu C, Su H, Hays J (2014) The sun attribute database: beyond categories for deeper scene understanding. Int J Comput Vis 108(1\u20132):59\u201381","journal-title":"Int J Comput Vis"},{"key":"1661_CR27","unstructured":"Romera-Paredes B, Torr P (2015) An embarrassingly simple approach to zero-shot learning. In: International conference on machine learning, pp. 2152\u20132161. PMLR (2015)"},{"key":"1661_CR28","unstructured":"Socher R, Ganjoo M, Manning C.D, Ng A (2013) Zero-shot learning through cross-modal transfer. In NIPS"},{"key":"1661_CR29","unstructured":"Wah C, Branson S, Welinder P, Perona P, Belongie S (2011) The caltech-ucsd birds-200-2011 dataset"},{"key":"1661_CR30","doi-asserted-by":"crossref","unstructured":"Xian Y, Akata Z, Sharma G, Nguyen Q, Hein M, Schiele B (2016) Latent embeddings for zero-shot classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 69\u201377","DOI":"10.1109\/CVPR.2016.15"},{"issue":"9","key":"1661_CR31","doi-asserted-by":"publisher","first-page":"2251","DOI":"10.1109\/TPAMI.2018.2857768","volume":"41","author":"Y Xian","year":"2018","unstructured":"Xian Y, Lampert CH, Schiele B, Akata Z (2018) Zero-shot learning-a comprehensive evaluation of the good, the bad and the ugly. IEEE Trans Pattern Anal Mach Intell 41(9):2251\u20132265","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"1661_CR32","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/s13042-020-01170-y","volume":"12","author":"Z Xie","year":"2021","unstructured":"Xie Z, Cao W, Ming Z (2021) A further study on biologically inspired feature enhancement in zero-shot learning. Int J Mach Learn Cybernet 12(1):257\u2013269","journal-title":"Int J Mach Learn Cybernet"},{"key":"1661_CR33","doi-asserted-by":"crossref","unstructured":"Zhang C, Wu T, Zhang Y, Zhao B, Wang T, Cui C, Yin Y (2021) Deep semantic-aware network for zero-shot visual urban perception. International Journal of Machine Learning and Cybernetics pp. 1\u201315","DOI":"10.1007\/s13042-021-01401-w"},{"key":"1661_CR34","doi-asserted-by":"crossref","unstructured":"Zhang H, Koniusz P (2018) Zero-shot kernel learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7670\u20137679","DOI":"10.1109\/CVPR.2018.00800"},{"issue":"1","key":"1661_CR35","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1109\/TIP.2018.2869696","volume":"28","author":"H Zhang","year":"2019","unstructured":"Zhang H, Long Y, Guan Y, Shao L (2019) Triple verification network for generalized zero-shot learning. IEEE Trans Image Process 28(1):506\u2013517","journal-title":"IEEE Trans Image Process"},{"issue":"20","key":"1661_CR36","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1049\/el.2018.5027","volume":"54","author":"H Zhang","year":"2018","unstructured":"Zhang H, Long Y, Zhao C (2018) Attribute relaxation from class level to instance level for zero-shot learning. Electron Lett 54(20):1170\u20131172","journal-title":"Electron Lett"},{"key":"1661_CR37","doi-asserted-by":"crossref","unstructured":"Zhang Z, Saligrama V (2016) Zero-shot learning via joint latent similarity embedding. In: proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6034\u20136042","DOI":"10.1109\/CVPR.2016.649"},{"key":"1661_CR38","doi-asserted-by":"crossref","unstructured":"Zhang Z, Xie Y, Yang L (2018) Photographic text-to-image synthesis with a hierarchically-nested adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6199\u20136208","DOI":"10.1109\/CVPR.2018.00649"},{"key":"1661_CR39","unstructured":"Zhou Y, Jin R, Hoi S.C.H (2010) Exclusive lasso for multi-task feature selection. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp. 988\u2013995. JMLR Workshop and Conference Proceedings"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01661-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-022-01661-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01661-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T03:48:10Z","timestamp":1677037690000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-022-01661-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,25]]},"references-count":39,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["1661"],"URL":"https:\/\/doi.org\/10.1007\/s13042-022-01661-0","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,25]]},"assertion":[{"value":"16 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}