{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T18:34:43Z","timestamp":1775241283175,"version":"3.50.1"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,8,9]],"date-time":"2025-08-09T00:00:00Z","timestamp":1754697600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,9]],"date-time":"2025-08-09T00:00:00Z","timestamp":1754697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012659","name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72061147004"],"award-info":[{"award-number":["72061147004"]}],"id":[{"id":"10.13039\/501100012659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012659","name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72061147004"],"award-info":[{"award-number":["72061147004"]}],"id":[{"id":"10.13039\/501100012659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012659","name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72061147004"],"award-info":[{"award-number":["72061147004"]}],"id":[{"id":"10.13039\/501100012659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012659","name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72061147004"],"award-info":[{"award-number":["72061147004"]}],"id":[{"id":"10.13039\/501100012659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001634","name":"University of Galway","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001634","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s13042-025-02730-w","type":"journal-article","created":{"date-parts":[[2025,8,9]],"date-time":"2025-08-09T16:15:53Z","timestamp":1754756153000},"page":"8401-8427","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["GCDPL-Net: graph-based co-attention discriminative prototype learning networks for few-shot classification"],"prefix":"10.1007","volume":"16","author":[{"given":"Mohammed","family":"Al-Habib","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zuping","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdulrahman","family":"Noman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raeed","family":"Al-sabri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Majjed","family":"Al-Qatf","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,9]]},"reference":[{"key":"2730_CR1","unstructured":"Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 4080\u20134090"},{"key":"2730_CR2","doi-asserted-by":"crossref","unstructured":"Gidaris S, Komodakis N (2019) Generating classification weights with gnn denoising autoencoders for few-shot learning. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 21\u201330","DOI":"10.1109\/CVPR.2019.00011"},{"issue":"12","key":"2730_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3659943","volume":"56","author":"H Gharoun","year":"2024","unstructured":"Gharoun H, Momenifar F, Chen F, Gandomi A (2024) Meta-learning approaches for few-shot learning: A survey of recent advances. ACM Comput Surv 56(12):1\u201341","journal-title":"ACM Comput Surv"},{"issue":"7","key":"2730_CR4","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1145\/3448250","volume":"64","author":"Y Bengio","year":"2021","unstructured":"Bengio Y, Lecun Y, Hinton G (2021) Deep learning for ai. Commun ACM 64(7):58\u201365","journal-title":"Commun ACM"},{"issue":"5","key":"2730_CR5","doi-asserted-by":"publisher","first-page":"1783","DOI":"10.1007\/s13042-022-01727-z","volume":"14","author":"K Wang","year":"2023","unstructured":"Wang K, Wang X, Cheng Y (2023) Few-shot learning based on enhanced pseudo-labels and graded pseudo-labeled data selection. Int J Mach Learn Cybern 14(5):1783\u20131795","journal-title":"Int J Mach Learn Cybern"},{"key":"2730_CR6","unstructured":"Satorras VG, Estrach JB (2018) Few-shot learning with graph neural networks. In: International Conference on Learning Representations"},{"key":"2730_CR7","doi-asserted-by":"crossref","unstructured":"Yang L, Li L, Zhang Z, Zhou X, Zhou E, Liu Y (2020) Dpgn: Distribution propagation graph network for few-shot learning. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 13390\u201313399","DOI":"10.1109\/CVPR42600.2020.01340"},{"key":"2730_CR8","unstructured":"Vinyals O, Blundell C, Lillicrap T, Wierstra D, et al (2016) Matching networks for one shot learning. In: Advances in Neural Information Processing Systems, pp. 3637\u20133645"},{"key":"2730_CR9","unstructured":"Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: Int. Conf. Mach. Learn., pp. 1126\u20131135. PMLR"},{"key":"2730_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122741","volume":"242","author":"Y Wang","year":"2024","unstructured":"Wang Y, Ji Y, Wang W, Wang B (2024) Bi-channel attention meta learning for few-shot fine-grained image recognition. Expert Syst Appl 242:122741","journal-title":"Expert Syst Appl"},{"key":"2730_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109235","volume":"136","author":"L Zhang","year":"2023","unstructured":"Zhang L, Zhou F, Wei W, Zhang Y (2023) Meta-hallucinating prototype for few-shot learning promotion. Pattern Recogn 136:109235","journal-title":"Pattern Recogn"},{"key":"2730_CR12","doi-asserted-by":"crossref","unstructured":"Sun Z, Zheng W, Guo P (2024) Klsanet: Key local semantic alignment network for few-shot image classification. Neural Networks, 106456","DOI":"10.1016\/j.neunet.2024.106456"},{"key":"2730_CR13","doi-asserted-by":"crossref","unstructured":"Li Z, Wang Y (2023) Better integrating vision and semantics for improving few-shot classification. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 4737\u20134746","DOI":"10.1145\/3581783.3613819"},{"key":"2730_CR14","doi-asserted-by":"crossref","unstructured":"Liu J, Song L, Qin Y (2020) Prototype rectification for few-shot learning. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part I 16, pp. 741\u2013756. Springer","DOI":"10.1007\/978-3-030-58452-8_43"},{"key":"2730_CR15","doi-asserted-by":"crossref","unstructured":"Fu M, Wang X, Wang J, Yi Z (2024) Prototype bayesian meta-learning for few-shot image classification. IEEE Transactions on Neural Networks and Learning Systems","DOI":"10.1109\/TNNLS.2024.3403865"},{"key":"2730_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123586","volume":"249","author":"P Zhao","year":"2024","unstructured":"Zhao P, Wang L, Zhao X, Liu H, Ji X (2024) Few-shot learning based on prototype rectification with a self-attention mechanism. Expert Syst Appl 249:123586","journal-title":"Expert Syst Appl"},{"key":"2730_CR17","doi-asserted-by":"crossref","unstructured":"Kim J, Kim T, Kim S, Yoo CD (2019) Edge-labeling graph neural network for few-shot learning. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 11\u201320","DOI":"10.1109\/CVPR.2019.00010"},{"key":"2730_CR18","unstructured":"Liu Y, Lee J, Park M, Kim S, Yang E, Hwang S, Yang Y (2019) Learning to propagate labels: Transductive propagation network for few-shot learning. In: International Conference on Learning Representations"},{"key":"2730_CR19","doi-asserted-by":"crossref","unstructured":"Zhu H, Koniusz P (2023) Transductive few-shot learning with prototype-based label propagation by iterative graph refinement. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 23996\u201324006","DOI":"10.1109\/CVPR52729.2023.02298"},{"key":"2730_CR20","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1109\/TMM.2022.3141886","volume":"25","author":"X Zhong","year":"2023","unstructured":"Zhong X, Gu C, Ye M, Huang W, Lin C-W (2023) Graph complemented latent representation for few-shot image classification. IEEE Trans Multimedia 25:1979\u20131990","journal-title":"IEEE Trans Multimedia"},{"issue":"3","key":"2730_CR21","doi-asserted-by":"publisher","first-page":"3306","DOI":"10.1109\/TII.2023.3306929","volume":"20","author":"S Fu","year":"2024","unstructured":"Fu S, Cao Q, Lei Y, Zhong Y, Zhan Y, You X (2024) Few-shot learning with dynamic graph structure preserving. IEEE Trans Indus Inform 20(3):3306\u20133315","journal-title":"IEEE Trans Indus Inform"},{"key":"2730_CR22","unstructured":"Ren M, Triantafillou E, Ravi S, Snell J, Swersky K, Tenenbaum JB, Larochelle H, Zemel RS (2018) Meta-learning for semi-supervised few-shot classification. arXiv preprint arXiv:1803.00676"},{"key":"2730_CR23","doi-asserted-by":"crossref","unstructured":"Zheng P (2024) Edge-labeling based modified gated graph network for few-shot learning. Pattern Recognition 150, 110264","DOI":"10.1016\/j.patcog.2024.110264"},{"key":"2730_CR24","first-page":"5998","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A (2017) Attention is all you need. Adv Neural Inform Process Syst 30:5998\u20136008","journal-title":"Adv Neural Inform Process Syst"},{"key":"2730_CR25","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803","DOI":"10.1109\/CVPR.2018.00813"},{"key":"2730_CR26","doi-asserted-by":"crossref","unstructured":"Sung F, Yang Y, Zhang L, Xiang T, Torr PH, Hospedales TM (2018) Learning to compare: Relation network for few-shot learning. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 1199\u20131208","DOI":"10.1109\/CVPR.2018.00131"},{"key":"2730_CR27","unstructured":"Oreshkin B, Rodr\u00edguez\u00a0L\u00f3pez P, Lacoste A (2018) Tadam: Task dependent adaptive metric for improved few-shot learning. In: Advances in Neural Information Processing Systems, pp. 719\u2013729"},{"key":"2730_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106289","volume":"175","author":"D Chen","year":"2024","unstructured":"Chen D, Chen F, Ouyang D, Shao J (2024) Mutual correlation network for few-shot learning. Neural Netw 175:106289","journal-title":"Neural Netw"},{"key":"2730_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109170","volume":"135","author":"X Huang","year":"2023","unstructured":"Huang X, Choi SH (2023) Sapenet: Self-attention based prototype enhancement network for few-shot learning. Pattern Recogn 135:109170","journal-title":"Pattern Recogn"},{"key":"2730_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107675","volume":"111","author":"Z Ma","year":"2021","unstructured":"Ma Z, Chen S (2021) Expand globally, shrink locally: Discriminant multi-label learning with missing labels. Pattern Recogn 111:107675","journal-title":"Pattern Recogn"},{"key":"2730_CR31","doi-asserted-by":"crossref","unstructured":"Zhou F, Cao C, Zhang K, Trajcevski G, Zhong T, Geng J (2019) Meta-gnn: On few-shot node classification in graph meta-learning. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, China, pp. 2357\u20132360","DOI":"10.1145\/3357384.3358106"},{"key":"2730_CR32","doi-asserted-by":"crossref","unstructured":"Tang S, Chen D, Bai L, Liu K, Ge Y, Ouyang W (2021) Mutual crf-gnn for few-shot learning. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 2329\u20132339","DOI":"10.1109\/CVPR46437.2021.00236"},{"key":"2730_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110367","volume":"151","author":"Y Huang","year":"2024","unstructured":"Huang Y, Hao H, Ge W, Cao Y, Wu M, Zhang C, Guo J (2024) Relation fusion propagation network for transductive few-shot learning. Pattern Recogn 151:110367","journal-title":"Pattern Recogn"},{"issue":"4","key":"2730_CR34","first-page":"1","volume":"40","author":"H Peng","year":"2021","unstructured":"Peng H, Zhang R, Dou Y, Yang R, Zhang J, Yu PS (2021) Reinforced neighborhood selection guided multi-relational graph neural networks. ACM Trans Inform Syst (TOIS) 40(4):1\u201346","journal-title":"ACM Trans Inform Syst (TOIS)"},{"issue":"1","key":"2730_CR35","doi-asserted-by":"publisher","first-page":"980","DOI":"10.1109\/TPAMI.2022.3144993","volume":"45","author":"H Peng","year":"2022","unstructured":"Peng H, Zhang R, Li S, Cao Y, Pan S, Philip SY (2022) Reinforced, incremental and cross-lingual event detection from social messages. IEEE Trans Pattern Anal Mach Intell 45(1):980\u2013998","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2730_CR36","doi-asserted-by":"crossref","unstructured":"Tao G, Weichao L, Yanmin H, Yu L (2021) Graph-based prototypical network for few-shot learning. In: 2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pp. 234\u2013237. IEEE","DOI":"10.1109\/ICCWAMTIP53232.2021.9674120"},{"key":"2730_CR37","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.patrec.2023.11.017","volume":"176","author":"Y Zhou","year":"2023","unstructured":"Zhou Y, Yu L (2023) Few-shot learning via weighted prototypes from graph structure. Pattern Recogn Lett 176:230\u2013235","journal-title":"Pattern Recogn Lett"},{"issue":"2","key":"2730_CR38","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1109\/TCDS.2022.3187216","volume":"15","author":"Q Wang","year":"2023","unstructured":"Wang Q, Ling H, Zhang B, Li P, Li Z, Shi Y, Zhao C, Zhao C (2023) Bidirectional gated edge-labeling graph recurrent neural network for few-shot learning. IEEE Trans Cogn Develop Syst 15(2):855\u2013864","journal-title":"IEEE Trans Cogn Develop Syst"},{"key":"2730_CR39","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"2730_CR40","unstructured":"Wah C, Branson S, Welinder P, Perona P, Belongie S (2011) The caltech-ucsd birds-200-2011 dataset"},{"key":"2730_CR41","unstructured":"Bertinetto L, Henriques JF, Torr PHS, Vedaldi A (2019) Meta-learning with differentiable closed-form solvers. In: Proceedings of the 7th International Conference on Learning Representations (ICLR)"},{"key":"2730_CR42","doi-asserted-by":"crossref","unstructured":"He J, Hong R, Liu X, Xu M, Sun Q (2022) Revisiting local descriptor for improved few-shot classification. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 18(2s), 1\u201323","DOI":"10.1145\/3511917"},{"key":"2730_CR43","doi-asserted-by":"crossref","unstructured":"Afrasiyabi A, Larochelle H, Lalonde J-F, Gagn\u00e9 C (2022) Matching feature sets for few-shot image classification. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 9014\u20139024","DOI":"10.1109\/CVPR52688.2022.00881"},{"key":"2730_CR44","doi-asserted-by":"crossref","unstructured":"Xu J, Le H, Huang M, Athar S, Samaras D (2021) Variational feature disentangling for fine-grained few-shot classification. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 8812\u20138821","DOI":"10.1109\/ICCV48922.2021.00869"},{"key":"2730_CR45","doi-asserted-by":"crossref","unstructured":"Xu S-L, Zhang F, Wei X-S, Wang J (2022) Dual attention networks for few-shot fine-grained recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 2911\u20132919","DOI":"10.1609\/aaai.v36i3.20196"},{"key":"2730_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110412","volume":"266","author":"Z Zheng","year":"2023","unstructured":"Zheng Z, Wu H, Lv L, Ye H, Zhang C, Yu G (2023) Iccl: Independent and correlative correspondence learning for few-shot image classification. Knowl Based Syst 266:110412","journal-title":"Knowl Based Syst"},{"issue":"1","key":"2730_CR47","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1007\/s10489-022-03479-3","volume":"53","author":"Z Zheng","year":"2023","unstructured":"Zheng Z, Feng X, Yu H, Li X, Gao M (2023) Bdla: Bi-directional local alignment for few-shot learning. Appl Intell 53(1):769\u2013785","journal-title":"Appl Intell"},{"issue":"12","key":"2730_CR48","doi-asserted-by":"publisher","first-page":"7789","DOI":"10.1109\/TCSVT.2023.3282777","volume":"33","author":"X Wang","year":"2023","unstructured":"Wang X, Wang X, Jiang B, Luo B (2023) Few-shot learning meets transformer: Unified query-support transformers for few-shot classification. IEEE Trans Circuits Syst Video Technol 33(12):7789\u20137802","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"21","key":"2730_CR49","doi-asserted-by":"publisher","first-page":"32991","DOI":"10.1007\/s11042-023-14413-1","volume":"82","author":"M Abdelaziz","year":"2023","unstructured":"Abdelaziz M, Zhang Z (2023) Learn to aggregate global and local representations for few-shot learning. Multimedia Tools Appl 82(21):32991\u201333014","journal-title":"Multimedia Tools Appl"},{"issue":"4","key":"2730_CR50","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s00138-024-01551-1","volume":"35","author":"Q Song","year":"2024","unstructured":"Song Q, Zhou S, Chen D (2024) Learning more discriminative local descriptors with parameter-free weighted attention for few-shot learning. Mach Vis Appl 35(4):71","journal-title":"Mach Vis Appl"},{"key":"2730_CR51","doi-asserted-by":"publisher","first-page":"2111","DOI":"10.1109\/TMM.2022.3142955","volume":"25","author":"H Zhang","year":"2023","unstructured":"Zhang H, Li H, Koniusz P (2023) Multi-level second-order few-shot learning. IEEE Trans Multimedia 25:2111\u20132126","journal-title":"IEEE Trans Multimedia"},{"key":"2730_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106600","volume":"180","author":"Y Liu","year":"2024","unstructured":"Liu Y, Shi D, Lin H (2024) Few-shot learning with representative global prototype. Neural Netw 180:106600","journal-title":"Neural Netw"},{"issue":"3","key":"2730_CR53","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.1109\/TPAMI.2022.3154930","volume":"46","author":"Y Du","year":"2024","unstructured":"Du Y, Sun H, Zhen X, Xu J, Yin Y, Shao L, Snoek CG (2024) Metakernel: Learning variational random features with limited labels. IEEE Trans Pattern Anal Mach Intell 46(3):1464\u20131478","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2730_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102611","volume":"113","author":"Z Sun","year":"2025","unstructured":"Sun Z, Zheng W, Guo P, Wang M (2025) Tst_mfl: Two-stage training based metric fusion learning for few-shot image classification. Inform Fusion 113:102611","journal-title":"Inform Fusion"},{"key":"2730_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109677","volume":"142","author":"H Chen","year":"2023","unstructured":"Chen H, Li L, Hu F, Lyu F, Zhao L, Huang K, Feng W, Xia Z (2023) Multi-semantic hypergraph neural network for effective few-shot learning. Pattern Recogn 142:109677","journal-title":"Pattern Recogn"},{"key":"2730_CR56","doi-asserted-by":"crossref","unstructured":"Shirekar OK, Singh A, Jamali-Rad H (2023) Self-attention message passing for contrastive few-shot learning. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 5426\u20135436","DOI":"10.1109\/WACV56688.2023.00539"},{"key":"2730_CR57","doi-asserted-by":"crossref","unstructured":"Zhao H, Su Y, Wu Z, Ding W (2024) Csts: Exploring class-specific and task-shared embedding representation for few-shot learning. IEEE Transactions on Neural Networks and Learning Systems","DOI":"10.1109\/TNNLS.2024.3380833"},{"issue":"4","key":"2730_CR58","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1007\/s11063-024-11638-5","volume":"56","author":"W Cao","year":"2024","unstructured":"Cao W, Li T, Liu Q, He Z (2024) Panet: Pluralistic attention network for few-shot image classification. Neural Process Lett 56(4):209","journal-title":"Neural Process Lett"},{"issue":"9","key":"2730_CR59","doi-asserted-by":"publisher","first-page":"5062","DOI":"10.1109\/TCSVT.2023.3248798","volume":"33","author":"F Zhou","year":"2023","unstructured":"Zhou F, Wei W, Zhang L, Zhang Y (2023) Learning to class-adaptively manipulate embeddings for few-shot learning. IEEE Trans Circuits Syst Video Technol 33(9):5062\u20135075","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"2730_CR60","doi-asserted-by":"crossref","unstructured":"Huang J, Zhao L, Yang H (2024) Local descriptor-based spatial cross attention network for few-shot learning. International Journal of Machine Learning and Cybernetics, 1\u201313","DOI":"10.1007\/s13042-024-02189-1"},{"issue":"4","key":"2730_CR61","doi-asserted-by":"publisher","first-page":"4343","DOI":"10.1007\/s12652-023-04545-5","volume":"14","author":"P Weng","year":"2023","unstructured":"Weng P, Dong S, Ren L, Zou K (2023) Local feature graph neural network for few-shot learning. J Ambient Intell Human Comput 14(4):4343\u20134354","journal-title":"J Ambient Intell Human Comput"},{"issue":"03","key":"2730_CR62","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1109\/TPAMI.2020.3018506","volume":"44","author":"Q Sun","year":"2022","unstructured":"Sun Q, Liu Y, Chen Z, Chua T-S, Schiele B (2022) Meta-transfer learning through hard tasks. IEEE Trans Pattern Anal Mach Intell 44(03):1443\u20131456","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2730_CR63","first-page":"5632","volume":"45","author":"C Zhang","year":"2023","unstructured":"Zhang C, Cai Y, Lin G, Shen C (2023) Deepemd: Differentiable earth mover\u2019s distance for few-shot learning. IEEE Trans Pattern Anal Mach Intell 45:5632\u20135648","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2730_CR64","doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02730-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02730-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02730-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T17:00:59Z","timestamp":1760547659000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02730-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,9]]},"references-count":64,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["2730"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02730-w","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,9]]},"assertion":[{"value":"29 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2025","order":3,"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 Conflict of interest to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}