{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T08:46:49Z","timestamp":1742978809183,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":47,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819609079"},{"type":"electronic","value":"9789819609086"}],"license":[{"start":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T00:00:00Z","timestamp":1733529600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T00:00:00Z","timestamp":1733529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-0908-6_14","type":"book-chapter","created":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T19:25:13Z","timestamp":1733513113000},"page":"239-256","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Do They Share the\u00a0Same Tail? Learning Individual Compositional Attribute Prototype for\u00a0Generalized Zero-Shot Learning"],"prefix":"10.1007","author":[{"given":"Yuyan","family":"Shi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenyi","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Run","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haofeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,7]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Atzmon, Y., Chechik, G.: Adaptive confidence smoothing for generalized zero-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 11671\u201311680 (2019)","DOI":"10.1109\/CVPR.2019.01194"},{"key":"14_CR2","unstructured":"Baroni, M., Zamparelli, R.: Nouns are vectors, adjectives are matrices: Representing adjective-noun constructions in semantic space. In: Proceedings of the 2010 conference on empirical methods in natural language processing. pp. 1183\u20131193 (2010)"},{"key":"14_CR3","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1162\/tacl_a_00051","volume":"5","author":"P Bojanowski","year":"2017","unstructured":"Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Transactions of the association for computational linguistics 5, 135\u2013146 (2017)","journal-title":"Transactions of the association for computational linguistics"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Chao, W.L., Changpinyo, S., Gong, B., Sha, F.: An empirical study and analysis of generalized zero-shot learning for object recognition in the wild. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14. pp. 52\u201368. Springer (2016)","DOI":"10.1007\/978-3-319-46475-6_4"},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Chen, C.Y., Grauman, K.: Inferring analogous attributes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 200\u2013207 (2014)","DOI":"10.1109\/CVPR.2014.33"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Chen, S., Hong, Z., Liu, Y., Xie, G.S., Sun, B., Li, H., Peng, Q., Lu, K., You, X.: Transzero: Attribute-guided transformer for zero-shot learning. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a036, pp. 330\u2013338 (2022)","DOI":"10.1609\/aaai.v36i1.19909"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhang, P., Li, J., Wang, S., Huang, Z.: Zero-shot learning by harnessing adversarial samples. In: Proceedings of the 31st ACM International Conference on Multimedia. pp. 4138\u20134146 (2023)","DOI":"10.1145\/3581783.3611823"},{"key":"14_CR8","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"14_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01231-1_2","volume-title":"Multi-modal cycle-consistent generalized zero-shot learning","author":"R Felix","year":"2018","unstructured":"Felix, R., Kumar, B., Reid, I., Carneiro, G.: Multi-modal cycle-consistent generalized zero-shot learning. Cornell University - arXiv, Cornell University - arXiv (Aug (2018)"},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Feng, Y., Huang, X., Yang, P., Yu, J., Sang, J.: Non-generative generalized zero-shot learning via task-correlated disentanglement and controllable samples synthesis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 9346\u20139355 (2022)","DOI":"10.1109\/CVPR52688.2022.00913"},{"key":"14_CR11","unstructured":"Guevara, E.R.: A regression model of adjective-noun compositionality in distributional semantics. In: Proceedings of the 2010 workshop on geometrical models of natural language semantics. pp. 33\u201337 (2010)"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Han, Z., Fu, Z., Chen, S., Yang, J.: Contrastive embedding for generalized zero-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 2371\u20132381 (2021)","DOI":"10.1109\/CVPR46437.2021.00240"},{"issue":"11","key":"14_CR13","doi-asserted-by":"publisher","first-page":"2606","DOI":"10.1007\/s11263-022-01656-y","volume":"130","author":"Z Han","year":"2022","unstructured":"Han, Z., Fu, Z., Chen, S., Yang, J.: Semantic contrastive embedding for generalized zero-shot learning. Int. J. Comput. Vision 130(11), 2606\u20132622 (2022)","journal-title":"Int. J. Comput. Vision"},{"issue":"5","key":"14_CR14","doi-asserted-by":"publisher","first-page":"3180","DOI":"10.1109\/TCSVT.2023.3313727","volume":"34","author":"Y Hu","year":"2024","unstructured":"Hu, Y., Feng, L., Jiang, H., Liu, M., Yin, B.: Domain-aware prototype network for generalized zero-shot learning. IEEE Trans. Circuits Syst. Video Technol. 34(5), 3180\u20133191 (2024)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Huynh, D., Elhamifar, E.: Fine-grained generalized zero-shot learning via dense attribute-based attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 4483\u20134493 (2020)","DOI":"10.1109\/CVPR42600.2020.00454"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Jiang, C., Shen, Y., Chen, D., Zhang, H., Shao, L., Torr, P.H.: Estimation of near-instance-level attribute bottleneck for zero-shot learning. International Journal of Computer Vision pp. 1\u201327 (2024)","DOI":"10.1007\/s11263-024-02021-x"},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Kong, X., Gao, Z., Li, X., Hong, M., Liu, J., Wang, C., Xie, Y., Qu, Y.: En-compactness: Self-distillation embedding & contrastive generation for generalized zero-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 9306\u20139315 (June 2022)","DOI":"10.1109\/CVPR52688.2022.00909"},{"key":"14_CR18","doi-asserted-by":"crossref","unstructured":"Lampert, C.H., Nickisch, H., Harmeling, S.: 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 (2009)","DOI":"10.1109\/CVPR.2009.5206594"},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Li, K., Min, M.R., Fu, Y.: Rethinking zero-shot learning: A conditional visual classification perspective. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 3583\u20133592 (2019)","DOI":"10.1109\/ICCV.2019.00368"},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Liu, Y., Guo, J., Cai, D., He, X.: Attribute attention for semantic disambiguation in zero-shot learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 6698\u20136707 (2019)","DOI":"10.1109\/ICCV.2019.00680"},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhou, L., Bai, X., Huang, Y., Gu, L., Zhou, J., Harada, T.: Goal-oriented gaze estimation for zero-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 3794\u20133803 (2021)","DOI":"10.1109\/CVPR46437.2021.00379"},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"Min, S., Yao, H., Xie, H., Wang, C., Zha, Z.J., Zhang, Y.: Domain-aware visual bias eliminating for generalized zero-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 12664\u201312673 (2020)","DOI":"10.1109\/CVPR42600.2020.01268"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Mishra, A., Krishna\u00a0Reddy, S., Mittal, A., Murthy, H.A.: A generative model for zero shot learning using conditional variational autoencoders. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. pp. 2188\u20132196 (2018)","DOI":"10.1109\/CVPRW.2018.00294"},{"key":"14_CR24","doi-asserted-by":"crossref","unstructured":"Misra, I., Gupta, A., Hebert, M.: From red wine to red tomato: Composition with context. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1792\u20131801 (2017)","DOI":"10.1109\/CVPR.2017.129"},{"key":"14_CR25","unstructured":"Mitchell, J., Lapata, M.: Vector-based models of semantic composition. In: proceedings of ACL-08: HLT. pp. 236\u2013244 (2008)"},{"key":"14_CR26","doi-asserted-by":"crossref","unstructured":"Nagarajan, T., Grauman, K.: Attributes as operators: factorizing unseen attribute-object compositions. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 169\u2013185 (2018)","DOI":"10.1007\/978-3-030-01246-5_11"},{"key":"14_CR27","doi-asserted-by":"crossref","unstructured":"Narayan, S., Gupta, A., Khan, F.S., Snoek, C.G., Shao, L.: Latent embedding feedback and discriminative features for zero-shot classification. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 479\u2013495. Springer (2020)","DOI":"10.1007\/978-3-030-58542-6_29"},{"key":"14_CR28","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748\u20138763. PMLR (2021)"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Sadeghi, M.A., Farhadi, A.: Recognition using visual phrases. IEEE (2011)","DOI":"10.1109\/CVPR.2011.5995711"},{"key":"14_CR30","doi-asserted-by":"crossref","unstructured":"Santa\u00a0Cruz, R., Fernando, B., Cherian, A., Gould, S.: Neural algebra of classifiers. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 729\u2013737. IEEE (2018)","DOI":"10.1109\/WACV.2018.00085"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Shen, Y., Qin, J., Huang, L., Liu, L., Zhu, F., Shao, L.: Invertible zero-shot recognition flows. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 614\u2013631. Springer (2020)","DOI":"10.1007\/978-3-030-58517-4_36"},{"key":"14_CR32","unstructured":"Socher, R., Ganjoo, M., Manning, C.D., Ng, A.: Zero-shot learning through cross-modal transfer. Advances in neural information processing systems 26 (2013)"},{"issue":"8","key":"14_CR33","doi-asserted-by":"publisher","first-page":"3774","DOI":"10.1109\/TCSVT.2023.3239390","volume":"33","author":"H Su","year":"2023","unstructured":"Su, H., Li, J., Lu, K., Zhu, L., Shen, H.T.: Dual-aligned feature confusion alleviation for generalized zero-shot learning. IEEE Trans. Circuits Syst. Video Technol. 33(8), 3774\u20133785 (2023)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"14_CR34","first-page":"2936","volume":"34","author":"C Wang","year":"2021","unstructured":"Wang, C., Min, S., Chen, X., Sun, X., Li, H.: Dual progressive prototype network for generalized zero-shot learning. Adv. Neural. Inf. Process. Syst. 34, 2936\u20132948 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"14_CR35","doi-asserted-by":"crossref","unstructured":"Xian, Y., Lorenz, T., Schiele, B., Akata, Z.: Feature generating networks for zero-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 5542\u20135551 (2018)","DOI":"10.1109\/CVPR.2018.00581"},{"key":"14_CR36","doi-asserted-by":"crossref","unstructured":"Xian, Y., Schiele, B., Akata, Z.: Zero-shot learning-the good, the bad and the ugly. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4582\u20134591 (2017)","DOI":"10.1109\/CVPR.2017.328"},{"key":"14_CR37","doi-asserted-by":"crossref","unstructured":"Xie, G.S., Liu, L., Jin, X., Zhu, F., Zhang, Z., Qin, J., Yao, Y., Shao, L.: Attentive region embedding network for zero-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 9384\u20139393 (2019)","DOI":"10.1109\/CVPR.2019.00961"},{"key":"14_CR38","doi-asserted-by":"crossref","unstructured":"Xie, G.S., Liu, L., Zhu, F., Zhao, F., Zhang, Z., Yao, Y., Qin, J., Shao, L.: Region graph embedding network for zero-shot learning. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 562\u2013580. Springer (2020)","DOI":"10.1007\/978-3-030-58548-8_33"},{"key":"14_CR39","first-page":"21969","volume":"33","author":"W Xu","year":"2020","unstructured":"Xu, W., Xian, Y., Wang, J., Schiele, B., Akata, Z.: Attribute prototype network for zero-shot learning. Adv. Neural. Inf. Process. Syst. 33, 21969\u201321980 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"6","key":"14_CR40","doi-asserted-by":"publisher","first-page":"1331","DOI":"10.1007\/s11263-023-01767-0","volume":"131","author":"FE Yang","year":"2023","unstructured":"Yang, F.E., Lee, Y.H., Lin, C.C., Wang, Y.C.F.: Semantics-guided intra-category knowledge transfer for generalized zero-shot learning. Int. J. Comput. Vision 131(6), 1331\u20131345 (2023)","journal-title":"Int. J. Comput. Vision"},{"key":"14_CR41","doi-asserted-by":"crossref","unstructured":"Yu, Y., Ji, Z., Han, J., Zhang, Z.: Episode-based prototype generating network for zero-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 14035\u201314044 (2020)","DOI":"10.1109\/CVPR42600.2020.01405"},{"key":"14_CR42","doi-asserted-by":"crossref","unstructured":"Yue, Z., Wang, T., Sun, Q., Hua, X.S., Zhang, H.: Counterfactual zero-shot and open-set visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 15404\u201315414 (2021)","DOI":"10.1109\/CVPR46437.2021.01515"},{"key":"14_CR43","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/157","volume-title":"Visual data synthesis via gan for zero-shot video classification","author":"C Zhang","year":"2018","unstructured":"Zhang, C., Peng, Y.: Visual data synthesis via gan for zero-shot video classification. Cornell University - arXiv, Cornell University - arXiv (Apr (2018)"},{"key":"14_CR44","doi-asserted-by":"crossref","unstructured":"Zhang, H., Kyaw, Z., Chang, S.F., Chua, T.S.: Visual translation embedding network for visual relation detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 5532\u20135540 (2017)","DOI":"10.1109\/CVPR.2017.331"},{"key":"14_CR45","doi-asserted-by":"crossref","unstructured":"Zhao, X., Shen, Y., Wang, S., Zhang, H.: Boosting generative zero-shot learning by synthesizing diverse features with attribute augmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a036, pp. 3454\u20133462 (2022)","DOI":"10.1609\/aaai.v36i3.20256"},{"key":"14_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109869","volume":"144","author":"L Zhou","year":"2023","unstructured":"Zhou, L., Liu, Y., Bai, X., Li, N., Yu, X., Zhou, J., Hancock, E.R.: Attribute subspaces for zero-shot learning. Pattern Recogn. 144, 109869 (2023)","journal-title":"Pattern Recogn."},{"key":"14_CR47","doi-asserted-by":"crossref","unstructured":"Zhu, P., Wang, H., Saligrama, V.: Generalized zero-shot recognition based on visually semantic embedding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 2995\u20133003 (2019)","DOI":"10.1109\/CVPR.2019.00311"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0908-6_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T20:11:15Z","timestamp":1733515875000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0908-6_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,7]]},"ISBN":["9789819609079","9789819609086"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0908-6_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,7]]},"assertion":[{"value":"7 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2024","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":"accv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}