{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T07:19:42Z","timestamp":1765610382644},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T00:00:00Z","timestamp":1694563200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T00:00:00Z","timestamp":1694563200000},"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":["J Supercomput"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s11227-023-05625-1","type":"journal-article","created":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T12:02:15Z","timestamp":1694606535000},"page":"4401-4419","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Dynamic visual-guided selection for zero-shot learning"],"prefix":"10.1007","volume":"80","author":[{"given":"Yuan","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Lei","family":"Xiang","sequence":"additional","affiliation":[]},{"given":"Fan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Haoran","family":"Duan","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Long","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,13]]},"reference":[{"key":"5625_CR1","doi-asserted-by":"crossref","unstructured":"Lampert CH, 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\/CVPRW.2009.5206594"},{"key":"5625_CR2","first-page":"22","volume":"2","author":"M Palatucci","year":"2009","unstructured":"Palatucci M, Pomerleau D, Hinton GE, Mitchell TM (2009) Zero-shot learning with semantic output codes. Adv Neural Inform Process Syst 2:22","journal-title":"Adv Neural Inform Process Syst"},{"issue":"3","key":"5625_CR3","first-page":"3848","volume":"45","author":"L Zhang","year":"2022","unstructured":"Zhang L, Chang X, Liu J, Luo M, Li Z, Yao L, Hauptmann A (2022) Tn-zstad: transferable network for zero-shot temporal activity detection. IEEE Trans Pattern Anal Mach Intell 45(3):3848\u20133861","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"12","key":"5625_CR4","doi-asserted-by":"publisher","first-page":"9733","DOI":"10.1109\/TPAMI.2021.3127346","volume":"44","author":"C Yan","year":"2021","unstructured":"Yan C, Chang X, Li Z, Guan W, Ge Z, Zhu L, Zheng Q (2021) Zeronas: differentiable generative adversarial networks search for zero-shot learning. IEEE Trans Pattern Anal Mach Intell 44(12):9733\u20139740","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5625_CR5","doi-asserted-by":"crossref","unstructured":"Chao W-L, Changpinyo S, Gong B, Sha F (2016) An empirical study and analysis of generalized zero-shot learning for object recognition in the wild. In European Conference on Computer Vision, pp 52\u201368. Springer","DOI":"10.1007\/978-3-319-46475-6_4"},{"issue":"9","key":"5625_CR6","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"},{"key":"5625_CR7","doi-asserted-by":"crossref","unstructured":"Kankuekul P, Kawewong A, Tangruamsub S, Hasegawa O (2012) Online incremental attribute-based zero-shot learning. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp 3657\u20133664. IEEE","DOI":"10.1109\/CVPR.2012.6248112"},{"issue":"7","key":"5625_CR8","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"},{"key":"5625_CR9","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"},{"key":"5625_CR10","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":"5625_CR11","doi-asserted-by":"crossref","unstructured":"Xie G-S, Liu L, Jin X, Zhu F, Zhang Z, Qin J, Yao Y, Shao L (2019) Attentive region embedding network for zero-shot learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 9384\u20139393","DOI":"10.1109\/CVPR.2019.00961"},{"key":"5625_CR12","first-page":"21969","volume":"33","author":"W Xu","year":"2020","unstructured":"Xu W, Xian Y, Wang J, Schiele B, Akata Z (2020) Attribute prototype network for zero-shot learning. Adv Neural Inf Process Syst 33:21969\u201321980","journal-title":"Adv Neural Inf Process Syst"},{"key":"5625_CR13","doi-asserted-by":"crossref","unstructured":"Chen S, Hong Z, Liu Y, Xie G-S, Sun B, Li H, Peng Q, Lu K, You X (2022) Transzero: attribute-guided transformer for zero-shot learning. In AAAI, vol 2, p 3","DOI":"10.1609\/aaai.v36i1.19909"},{"issue":"11","key":"5625_CR14","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139\u2013144","journal-title":"Commun ACM"},{"key":"5625_CR15","unstructured":"Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114"},{"key":"5625_CR16","doi-asserted-by":"crossref","unstructured":"Verma VK, Arora G, Mishra A, Rai P (2018) Generalized zero-shot learning via synthesized examples. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4281\u20134289","DOI":"10.1109\/CVPR.2018.00450"},{"key":"5625_CR17","doi-asserted-by":"crossref","unstructured":"Xian Y, Lorenz T, Schiele B, Akata Z (2018) Feature generating networks for zero-shot learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5542\u20135551","DOI":"10.1109\/CVPR.2018.00581"},{"key":"5625_CR18","doi-asserted-by":"crossref","unstructured":"Schonfeld E, Ebrahimi S, Sinha S, Darrell T, Akata Z (2019) Generalized zero-and few-shot learning via aligned variational autoencoders. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 8247\u20138255","DOI":"10.1109\/CVPR.2019.00844"},{"key":"5625_CR19","doi-asserted-by":"crossref","unstructured":"Yue Z, Wang T, Sun Q, Hua X-S, Zhang H (2021) Counterfactual zero-shot and open-set visual recognition. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 15404\u201315414","DOI":"10.1109\/CVPR46437.2021.01515"},{"key":"5625_CR20","doi-asserted-by":"crossref","unstructured":"Chen S, Wang W, Xia B, Peng Q, You X, Zheng F, Shao L (2021) Free: feature refinement for generalized zero-shot learning. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 122\u2013131","DOI":"10.1109\/ICCV48922.2021.00019"},{"key":"5625_CR21","unstructured":"Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, Zemel R, Bengio Y (2015) Show, attend and tell: neural image caption generation with visual attention. In International Conference on Machine Learning, pp 2048\u20132057. PMLR"},{"key":"5625_CR22","doi-asserted-by":"crossref","unstructured":"Xian Y, Schiele B, Akata Z (2017) 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","DOI":"10.1109\/CVPR.2017.328"},{"key":"5625_CR23","unstructured":"Welinder P, Branson S, Mita T, Wah C, Schroff F, Belongie S, Perona P (2010) Caltech-ucsd birds 200"},{"key":"5625_CR24","doi-asserted-by":"crossref","unstructured":"Patterson G, Hays J (2012) Sun attribute database: discovering, annotating, and recognizing scene attributes. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp 2751\u20132758. IEEE","DOI":"10.1109\/CVPR.2012.6247998"},{"key":"5625_CR25","doi-asserted-by":"crossref","unstructured":"Naeem MF, Khan MGZA, Xian Y, Afzal MZ, Stricker D, Van\u00a0Gool L, Tombari F (2023) I2mvformer: large language model generated multi-view document supervision for zero-shot image classification. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 15169\u201315179","DOI":"10.1109\/CVPR52729.2023.01456"},{"key":"5625_CR26","doi-asserted-by":"crossref","unstructured":"Huynh D, Elhamifar E (2020) 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","DOI":"10.1109\/CVPR42600.2020.00454"},{"key":"5625_CR27","unstructured":"Chou Y-Y, Lin H-T, Liu T-L (2020) Adaptive and generative zero-shot learning. In: International Conference on Learning Representations"},{"key":"5625_CR28","first-page":"2936","volume":"34","author":"C Wang","year":"2021","unstructured":"Wang C, Min S, Chen X, Sun X, Li H (2021) Dual progressive prototype network for generalized zero-shot learning. Adv Neural Inf Process Syst 34:2936\u20132948","journal-title":"Adv Neural Inf Process Syst"},{"key":"5625_CR29","doi-asserted-by":"crossref","unstructured":"Liu Y, Zhou L, Bai X, Huang Y, Gu L, Zhou J, Harada T (2021) Goal-oriented gaze estimation for zero-shot learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 3794\u20133803","DOI":"10.1109\/CVPR46437.2021.00379"},{"key":"5625_CR30","doi-asserted-by":"crossref","unstructured":"Chen S, Hong Z, Xie G-S, Yang W, Peng Q, Wang K, Zhao J, You X (2022) Msdn: mutually semantic distillation network for zero-shot learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 7612\u20137621","DOI":"10.1109\/CVPR52688.2022.00746"},{"key":"5625_CR31","doi-asserted-by":"crossref","unstructured":"Ge J, Xie H, Min S, Li P, Zhang Y (2022) Dual part discovery network for zero-shot learning. In Proceedings of the 30th ACM International Conference on Multimedia, pp 3244\u20133252","DOI":"10.1145\/3503161.3547889"},{"key":"5625_CR32","first-page":"56","volume":"31","author":"Z Ji","year":"2018","unstructured":"Ji Z, Fu Y, Guo J, Pang Y, Zhang ZM et al (2018) Stacked semantics-guided attention model for fine-grained zero-shot learning. Adv Neural Inform Process Syst 31:56","journal-title":"Adv Neural Inform Process Syst"},{"key":"5625_CR33","unstructured":"Alamri F, Dutta A (2021) Multi-head self-attention via vision transformer for zero-shot learning. arXiv preprint arXiv:2108.00045"},{"key":"5625_CR34","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al. (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"5625_CR35","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781"},{"key":"5625_CR36","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"5625_CR37","unstructured":"Naeem MF, Xian Y, Van\u00a0Gool L, Tombari F (2022) I2dformer: learning image to document attention for zero-shot image classification. arXiv preprint arXiv:2209.10304"},{"key":"5625_CR38","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A et al (2020) Language models are few-shot learners. Adv Neural Inf Process Syst 33:1877\u20131901","journal-title":"Adv Neural Inf Process Syst"},{"key":"5625_CR39","unstructured":"Zhang S, Roller S, Goyal N, Artetxe M, Chen M, Chen S, Dewan C, Diab M, Li X, Lin XV et al. (2022) Opt: open pre-trained transformer language models. arXiv preprint arXiv:2205.01068"},{"key":"5625_CR40","unstructured":"Chowdhery A, Narang S, Devlin J, Bosma M, Mishra G, Roberts A, Barham P, Chung HW, Sutton C, Gehrmann S et al. (2022) Palm: scaling language modeling with pathways. arXiv preprint arXiv:2204.02311"},{"key":"5625_CR41","doi-asserted-by":"crossref","unstructured":"Xu W, Xian Y, Wang J, Schiele B, Akata Z (2022) Vgse: visually-grounded semantic embeddings for zero-shot learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 9316\u20139325","DOI":"10.1109\/CVPR52688.2022.00910"},{"key":"5625_CR42","doi-asserted-by":"crossref","unstructured":"Liu M, Li F, Zhang C, Wei Y, Bai H, Zhao Y (2023) Progressive semantic-visual mutual adaption for generalized zero-shot learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 15337\u201315346","DOI":"10.1109\/CVPR52729.2023.01472"},{"key":"5625_CR43","unstructured":"Chou Y-Y, Lin H-T, Liu T-L (2021) Adaptive and generative zero-shot learning. In International Conference on Learning Representations"},{"key":"5625_CR44","doi-asserted-by":"crossref","unstructured":"Han Z, Fu Z, Chen S, Yang J (2021) Contrastive embedding for generalized zero-shot learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 2371\u20132381","DOI":"10.1109\/CVPR46437.2021.00240"},{"key":"5625_CR45","first-page":"16622","volume":"34","author":"S Chen","year":"2021","unstructured":"Chen S, Xie G, Liu Y, Peng Q, Sun B, Li H, You X, Shao L (2021) Hsva: hierarchical semantic-visual adaptation for zero-shot learning. Adv Neural Inf Process Syst 34:16622\u201316634","journal-title":"Adv Neural Inf Process Syst"},{"key":"5625_CR46","doi-asserted-by":"crossref","unstructured":"Li X, Xu Z, Wei K, Deng C (2021) Generalized zero-shot learning via disentangled representation. In Proceedings of the AAAI Conference on Artificial Intelligence, vol 35, pp 1966\u20131974","DOI":"10.1609\/aaai.v35i3.16292"},{"key":"5625_CR47","doi-asserted-by":"crossref","unstructured":"Ge J, Xie H, Min S, Zhang Y (2021) Semantic-guided reinforced region embedding for generalized zero-shot learning. In Proceedings of the AAAI Conference on Artificial Intelligence, vol 35, pp 1406\u20131414","DOI":"10.1609\/aaai.v35i2.16230"},{"key":"5625_CR48","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision, pp 618\u2013626","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05625-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-023-05625-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05625-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T11:19:50Z","timestamp":1705922390000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-023-05625-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,13]]},"references-count":48,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["5625"],"URL":"https:\/\/doi.org\/10.1007\/s11227-023-05625-1","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,13]]},"assertion":[{"value":"21 August 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and\/or discussion reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}