{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T03:37:25Z","timestamp":1776051445683,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Beijing Smart Agriculture Innovation Consortium Project","award":["BAIC10-2024"],"award-info":[{"award-number":["BAIC10-2024"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s10489-025-06581-4","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T01:39:19Z","timestamp":1746668359000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["DFPN: a dynamic fusion prototypical network for few-shot learning"],"prefix":"10.1007","volume":"55","author":[{"given":"Mengping","family":"Dong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenbo","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,8]]},"reference":[{"key":"6581_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107296","volume":"127","author":"J Jia","year":"2024","unstructured":"Jia J, Feng X, Yu H (2024) Few-shot classification via efficient meta-learning with hybrid optimization. Eng Appl Artif Intell 127:107296","journal-title":"Eng Appl Artif Intell"},{"key":"6581_CR2","doi-asserted-by":"crossref","unstructured":"Li F, Shen L, Mi Y, Li Z (2022) Drcnet: dynamic image restoration contrastive network. In: European conference on computer vision. Springer, pp 514\u2013532","DOI":"10.1007\/978-3-031-19800-7_30"},{"key":"6581_CR3","doi-asserted-by":"crossref","unstructured":"Li F, Zhang L, Liu Z, Lei J, Li Z (2023) Multi-frequency representation enhancement with privilege information for video super-resolution. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 12814\u201312825","DOI":"10.1109\/ICCV51070.2023.01177"},{"key":"6581_CR4","doi-asserted-by":"crossref","unstructured":"Hao F, He F, Liu L, Wu F, Tao D, Cheng J (2023) Class-aware patch embedding adaptation for few-shot image classification. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 18905\u201318915","DOI":"10.1109\/ICCV51070.2023.01733"},{"key":"6581_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109904","volume":"145","author":"F Liu","year":"2024","unstructured":"Liu F, Yang S, Chen D, Huang H, Zhou J (2024) Few-shot classification guided by generalization error bound. Pattern Recogn 145:109904","journal-title":"Pattern Recogn"},{"key":"6581_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109917","volume":"142","author":"M Dong","year":"2025","unstructured":"Dong M, Li F, Li Z, Liu X (2025) Contrastive prototype learning with semantic patchmix for few-shot image classification. Eng Appl Artif Intell 142:109917","journal-title":"Eng Appl Artif Intell"},{"key":"6581_CR7","first-page":"4077","volume":"30","author":"J Snell","year":"2017","unstructured":"Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. Adv Neural Inf Process Syst 30:4077\u20134087","journal-title":"Adv Neural Inf Process Syst"},{"key":"6581_CR8","doi-asserted-by":"crossref","unstructured":"Fu M, Wang X, Wang J, Yi Z (2024) Prototype bayesian meta-learning for few-shot image classification. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2024.3403865"},{"key":"6581_CR9","doi-asserted-by":"crossref","unstructured":"Liu J, Song L, Qin Y (2020) Prototype rectification for few-shot learning. In: European conference on computer vision, pp 741\u2013756","DOI":"10.1007\/978-3-030-58452-8_43"},{"key":"6581_CR10","unstructured":"Yang S, Liu L, Xu M (2021) Free lunch for few-shot learning: distribution calibration. In: International conference on learning representations"},{"key":"6581_CR11","doi-asserted-by":"crossref","unstructured":"Zhang B, Li X, Ye Y, Feng S (2023) Prototype completion for few-shot learning. IEEE Trans Pattern Anal Mach Intell, 1\u201316","DOI":"10.1109\/TPAMI.2022.3217373"},{"key":"6581_CR12","unstructured":"Weisberg S (2001) Yeo-johnson power transformations. Department of Applied Statistics, University of Minnesota. Retrieved June 1, 2003"},{"key":"6581_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.107915","volume":"132","author":"R Xu","year":"2024","unstructured":"Xu R, Shao S, Xing L, Wang Y, Liu B, Liu W (2024) Ensembling multi-view discriminative semantic feature for few-shot classification. Eng Appl Artif Intell 132:107915","journal-title":"Eng Appl Artif Intell"},{"key":"6581_CR14","unstructured":"Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference on machine learning, pp 1126\u20131135"},{"key":"6581_CR15","unstructured":"Sun S, Gao H (2023) Meta-adam: an meta-learned adaptive optimizer with momentum for few-shot learning. Adv Neural Inf Process Syst 37"},{"key":"6581_CR16","unstructured":"Baik S, Choi M, Choi J, Kim H, Lee KM (2023) Learning to learn task-adaptive hyperparameters for few-shot learning. IEEE Trans Pattern Anal Mach Intell, 1\u201313"},{"key":"6581_CR17","doi-asserted-by":"crossref","unstructured":"Tian Y, Wang Y, Krishnan D, Tenenbaum JB, Isola P (2020) Rethinking few-shot image classification: a good embedding is all you need? In: European conference on computer vision, pp 266\u2013282","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"6581_CR18","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: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9014\u20139024","DOI":"10.1109\/CVPR52688.2022.00881"},{"key":"6581_CR19","doi-asserted-by":"publisher","first-page":"111122","DOI":"10.1016\/j.patcog.2024.111122","volume":"159","author":"M Dong","year":"2025","unstructured":"Dong M, Li F, Li Z, Liu X (2025) Prsn: prototype resynthesis network with cross-image semantic alignment for few-shot image classification. Pattern Recogn 159:111122","journal-title":"Pattern Recogn"},{"key":"6581_CR20","first-page":"721","volume":"31","author":"B Oreshkin","year":"2018","unstructured":"Oreshkin B, Rodr\u00edguez L\u00f3pez P, Lacoste A (2018) Tadam: task dependent adaptive metric for improved few-shot learning. Adv Neural Inf Process Syst 31:721\u2013731","journal-title":"Adv Neural Inf Process Syst"},{"key":"6581_CR21","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":"6581_CR22","first-page":"3637","volume":"29","author":"O Vinyals","year":"2016","unstructured":"Vinyals O, Blundell C, Lillicrap T, Wierstra D (2016) Matching networks for one shot learning. Adv Neural Inf Process Syst 29:3637\u20133645","journal-title":"Adv Neural Inf Process Syst"},{"key":"6581_CR23","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: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1199\u20131208","DOI":"10.1109\/CVPR.2018.00131"},{"key":"6581_CR24","doi-asserted-by":"crossref","unstructured":"Chen H, Li H, Li Y, Chen C (2022) Multi-level metric learning for few-shot image recognition. In: International conference on artificial neural networks, pp 243\u2013254","DOI":"10.1007\/978-3-031-15919-0_21"},{"key":"6581_CR25","doi-asserted-by":"crossref","unstructured":"Zhang B, Jiang H, Feng S, Li X, Ye Y, Ye R (2022) Hyperbolic knowledge transfer with class hierarchy for few-shot learning. In: International joint conference on artificial intelligence, pp 3723\u20133729","DOI":"10.24963\/ijcai.2022\/517"},{"key":"6581_CR26","doi-asserted-by":"crossref","unstructured":"Zhang B, Li X, Ye Y, Huang Z, Zhang L (2021) Prototype completion with primitive knowledge for few-shot learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3754\u20133762","DOI":"10.1109\/CVPR46437.2021.00375"},{"key":"6581_CR27","doi-asserted-by":"crossref","unstructured":"Geng R, Li B, Li Y, Zhu X, Jian P, Sun J (2019) Induction networks for few-shot text classification. Conference on Empirical Methods in Natural Language Processing-International Joint Conference on Natural Language Processing, 3895\u20133904","DOI":"10.18653\/v1\/D19-1403"},{"key":"6581_CR28","first-page":"3856","volume":"30","author":"S Sabour","year":"2017","unstructured":"Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. Adv Neural Inf Process Syst 30:3856\u20133866","journal-title":"Adv Neural Inf Process Syst"},{"key":"6581_CR29","unstructured":"Ravi S, Larochelle H (2017) Optimization as a model for few-shot learning. In: International conference on learning representations"},{"key":"6581_CR30","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. In: International conference on learning representations"},{"key":"6581_CR31","unstructured":"Bertinetto L, Henriques JF, Torr PH, Vedaldi A (2019) Meta-learning with differentiable closed-form solvers. In: International conference on learning representations"},{"key":"6581_CR32","doi-asserted-by":"crossref","unstructured":"Lee K, Maji S, Ravichandran A, Soatto S (2019) Meta-learning with differentiable convex optimization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10657\u201310665","DOI":"10.1109\/CVPR.2019.01091"},{"key":"6581_CR33","doi-asserted-by":"crossref","unstructured":"Chen Y, Liu Z, Xu H, Darrell T, Wang X (2021) Meta-baseline: exploring simple meta-learning for few-shot learning. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 9062\u20139071","DOI":"10.1109\/ICCV48922.2021.00893"},{"key":"6581_CR34","doi-asserted-by":"crossref","unstructured":"Padmanabhan DC, Gowda S, Arani E, Zonooz B (2023) Lsfsl: leveraging shape information in few-shot learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4970\u20134979","DOI":"10.1109\/CVPRW59228.2023.00525"},{"key":"6581_CR35","first-page":"6996","volume":"35","author":"D Guo","year":"2022","unstructured":"Guo D, Tian L, Zhao H, Zhou M, Zha H (2022) Adaptive distribution calibration for few-shot learning with hierarchical optimal transport. Adv Neural Inf Process Syst 35:6996\u20137010","journal-title":"Adv Neural Inf Process Syst"},{"issue":"5","key":"6581_CR36","first-page":"5632","volume":"45","author":"C Zhang","year":"2022","unstructured":"Zhang C, Cai Y, Lin G, Shen C (2022) Deepemd: differentiable earth mover\u2019s distance for few-shot learning. IEEE Trans Pattern Anal Mach Intell 45(5):5632\u20135648","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6581_CR37","doi-asserted-by":"crossref","unstructured":"Ma R, Fang P, Drummond T, Harandi M (2022) Adaptive poincar\u00e9 point to set distance for few-shot classification. In: Proceedings of the AAAI conference on artificial intelligence, vol 36, pp 1926\u20131934","DOI":"10.1609\/aaai.v36i2.20087"},{"key":"6581_CR38","doi-asserted-by":"crossref","unstructured":"Xie J, Long F, Lv J, Wang Q, Li P (2022) Joint distribution matters: deep brownian distance covariance for few-shot classification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7972\u20137981","DOI":"10.1109\/CVPR52688.2022.00781"},{"key":"6581_CR39","doi-asserted-by":"crossref","unstructured":"Qiao Q, Xie Y, Zeng Z, Li F (2024) Talds-net: task-aware adaptive local descriptors selection for few-shot image classification. In: IEEE international conference on acoustics, speech and signal processing, pp 3750\u20133754","DOI":"10.1109\/ICASSP48485.2024.10448167"},{"key":"6581_CR40","doi-asserted-by":"crossref","unstructured":"Chen B, Zhou H, Liu Y, Zeng B, Lu G, Zhang Z (2024) Decoupled self-adaptive distribution regularization for few-shot image classification. In: IEEE international conference on acoustics, speech and signal processing, pp 5420\u20135424","DOI":"10.1109\/ICASSP48485.2024.10446597"},{"key":"6581_CR41","doi-asserted-by":"crossref","unstructured":"Guo Q, Haotong G, Wei X, Fu Y, Yu Y, Zhang W, Ge W (2023) Rankdnn: learning to rank for few-shot learning. In: Proceedings of the AAAI conference on artificial intelligence, vol 37, pp 728\u2013736","DOI":"10.1609\/aaai.v37i1.25150"},{"key":"6581_CR42","doi-asserted-by":"crossref","unstructured":"Zhang M, Huang S, Li W, Wang D (2022) Tree structure-aware few-shot image classification via hierarchical aggregation. In: European conference on computer vision, pp 453\u2013470","DOI":"10.1007\/978-3-031-20044-1_26"},{"key":"6581_CR43","doi-asserted-by":"crossref","unstructured":"Gao Z, Wu Y, Jia Y, Harandi M (2021) Curvature generation in curved spaces for few-shot learning. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 8691\u20138700","DOI":"10.1109\/ICCV48922.2021.00857"},{"key":"6581_CR44","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"},{"key":"6581_CR45","doi-asserted-by":"crossref","unstructured":"Liu Y, Zhang W, Xiang C, Zheng T, Cai D, He X (2022) Learning to affiliate: mutual centralized learning for few-shot classification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14411\u201314420","DOI":"10.1109\/CVPR52688.2022.01401"},{"key":"6581_CR46","doi-asserted-by":"crossref","unstructured":"Liu B-D, Shao S, Zhao C, Xing L, Liu W, Cao W, Zhou Y (2024) Few-shot image classification via hybrid representation. Pattern Recogn 155","DOI":"10.1016\/j.patcog.2024.110640"},{"key":"6581_CR47","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhang L, Yao Y, Fu Y (2021) How to trust unlabeled data instance credibility inference for few-shot learning. IEEE Trans Pattern Anal Mach Intell, 1\u201314","DOI":"10.1109\/CVPR42600.2020.01285"},{"key":"6581_CR48","first-page":"25932","volume":"34","author":"X Shen","year":"2021","unstructured":"Shen X, Xiao Y, Hu SX, Sbai O, Aubry M (2021) Re-ranking for image retrieval and transductive few-shot classification. Adv Neural Inf Process Syst 34:25932\u201325943","journal-title":"Adv Neural Inf Process Syst"},{"key":"6581_CR49","doi-asserted-by":"crossref","unstructured":"Ouali Y, Hudelot C, Tami M (2021) Spatial contrastive learning for few-shot classification. In: Joint European conference on machine learning and knowledge discovery in databases, pp 671\u2013686","DOI":"10.1007\/978-3-030-86486-6_41"},{"key":"6581_CR50","doi-asserted-by":"crossref","unstructured":"Lyu Q, Wang W (2023) Compositional prototypical networks for few-shot classification. In: Proceedings of the AAAI conference on artificial intelligence, vol 37, pp 9011\u20139019","DOI":"10.1609\/aaai.v37i7.26082"},{"key":"6581_CR51","doi-asserted-by":"crossref","unstructured":"Zhang Y, Huang S, Huangfu L, Zeng DD (2025) Learning feature exploration and selection with handcrafted features for few-shot learning. IEEE Trans Syst Man Cybern: Syst","DOI":"10.1109\/TSMC.2024.3524390"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06581-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06581-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06581-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T13:56:53Z","timestamp":1758290213000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06581-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,8]]},"references-count":51,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["6581"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06581-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,8]]},"assertion":[{"value":"16 April 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain studies with human participants or animals. Statement of informed consent is not applicable since the manuscript does not contain any patient data.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}],"article-number":"729"}}