{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:27:48Z","timestamp":1740122868559,"version":"3.37.3"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T00:00:00Z","timestamp":1693440000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T00:00:00Z","timestamp":1693440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971290, 61771322"],"award-info":[{"award-number":["61971290, 61771322"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Foundation of Shenzhen","award":["JCYJ20190808160815125"],"award-info":[{"award-number":["JCYJ20190808160815125"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-16551-y","type":"journal-article","created":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T07:03:01Z","timestamp":1693465381000},"page":"26527-26546","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dual-model Collaborative Learning with Knowledge Clustering for Few-shot Image Classification"],"prefix":"10.1007","volume":"83","author":[{"given":"Min","family":"Xiong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8174-6167","authenticated-orcid":false,"given":"Wenming","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhineng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,31]]},"reference":[{"key":"16551_CR1","doi-asserted-by":"crossref","unstructured":"Bhatti UA, Yu Z, Hasnain A, Nawaz SA, Yuan L, Wen L, Bhatti MA (2022) Evaluating the impact of roads on the diversity pattern and density of trees to improve the conservation of species. Envi Sci Pollut Res 1\u201311","DOI":"10.21203\/rs.3.rs-398246\/v1"},{"issue":"1","key":"16551_CR2","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1166\/jmihi.2021.3313","volume":"11","author":"UA Bhatti","year":"2021","unstructured":"Bhatti UA, Yuan L, Yu Z, Nawaz SA, Mehmood A, Bhatti MA, Nizamani MM, Xiao S et al (2021) Predictive data modeling using sp-knn for risk factor evaluation in urban demographical healthcare data. J Med Imaging Health Inform 11(1):7\u201314","journal-title":"J Med Imaging Health Inform"},{"key":"16551_CR3","doi-asserted-by":"publisher","first-page":"76386","DOI":"10.1109\/ACCESS.2020.2988298","volume":"8","author":"UA Bhatti","year":"2020","unstructured":"Bhatti UA, Yu Z, Li J, Nawaz SA, Mehmood A, Zhang K, Yuan L (2020) Hybrid watermarking algorithm using clifford algebra with arnold scrambling and chaotic encryption. IEEE Access 8:76386\u201376398","journal-title":"IEEE Access"},{"key":"16551_CR4","doi-asserted-by":"crossref","unstructured":"Bhatti UA, Yu Z, Yuan L, Nawaz SA, Aamir M, Bhatti MA (2022) A robust remote sensing image watermarking algorithm based on region-specific surf. In: Proceedings of International Conference on Information Technology and Applications: ICITA 2021, pp. 75\u201385. Springer","DOI":"10.1007\/978-981-16-7618-5_7"},{"key":"16551_CR5","doi-asserted-by":"publisher","first-page":"155783","DOI":"10.1109\/ACCESS.2020.3018544","volume":"8","author":"UA Bhatti","year":"2020","unstructured":"Bhatti UA, Yu Z, Yuan L, Zeeshan Z, Nawaz SA, Bhatti M, Mehmood A, Ain QU, Wen L (2020) Geometric algebra applications in geospatial artificial intelligence and remote sensing image processing. IEEE Access 8:155783\u2013155796","journal-title":"IEEE Access"},{"key":"16551_CR6","doi-asserted-by":"crossref","unstructured":"Wu Y, Liu H, Fu Y (2017) Low-shot face recognition with hybrid classifiers. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops","DOI":"10.1109\/ICCVW.2017.228"},{"key":"16551_CR7","doi-asserted-by":"crossref","unstructured":"Mahajan K, Sharma M, Vig L (2020) Metadermdiagnosis: Few-shot skin disease identification using meta-learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 730\u2013731","DOI":"10.1109\/CVPRW50498.2020.00373"},{"issue":"2","key":"16551_CR8","doi-asserted-by":"publisher","DOI":"10.1088\/2631-7990\/abe0d0","volume":"3","author":"Y Chen","year":"2021","unstructured":"Chen Y, Peng X, Kong L, Dong G, Remani A, Leach R (2021) Defect inspection technologies for additive manufacturing. Int J Extreme Manuf 3(2):022002. https:\/\/doi.org\/10.1088\/2631-7990\/abe0d0","journal-title":"Int J Extreme Manuf"},{"key":"16551_CR9","doi-asserted-by":"crossref","unstructured":"Chen C, Yang X, Xu C, Huang X, Ma Z (2021) Eckpn: Explicit class knowledge propagation network for transductive few-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6596\u20136605","DOI":"10.1109\/CVPR46437.2021.00653"},{"key":"16551_CR10","unstructured":"Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (Eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ???"},{"key":"16551_CR11","unstructured":"Chen Y, Wang X, Liu Z, Xu H, Darrell T (2020) A new meta-baseline for few-shot learning"},{"key":"16551_CR12","unstructured":"Weinberger KQ, Saul LK (2009) Distance metric learning for large margin nearest neighbor classification. J Mach Learn Res 10(2)"},{"key":"16551_CR13","doi-asserted-by":"crossref","unstructured":"Bateni P, Goyal R, Masrani V, Wood F, Sigal L (2020) Improved few-shot visual classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14493\u201314502","DOI":"10.1109\/CVPR42600.2020.01450"},{"key":"16551_CR14","unstructured":"Garcia V, Bruna J (2017) Few-shot learning with graph neural networks. arXiv preprint arXiv:1711.04043"},{"key":"16551_CR15","doi-asserted-by":"crossref","unstructured":"Liu G, Zhao L, Li W, Guo D, Fang X (2021) Class-wise metric scaling for improved few-shot classification. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 586\u2013595","DOI":"10.1109\/WACV48630.2021.00063"},{"key":"16551_CR16","unstructured":"Finn C, Abbeel P, Levine S (2017) Modelagnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126\u20131135. PMLR"},{"issue":"6","key":"16551_CR17","doi-asserted-by":"publisher","first-page":"3974","DOI":"10.1109\/TII.2019.2939278","volume":"16","author":"X Yan","year":"2019","unstructured":"Yan X, Ye Y, Qiu X, Manic M, Yu H (2019) Cmib: unsupervised image object categorization in multiple visual contexts. IEEE Trans Ind Inform 16(6):3974\u20133986","journal-title":"IEEE Trans Ind Inform"},{"key":"16551_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2022.01.065","volume":"593","author":"X Yan","year":"2022","unstructured":"Yan X, Mao Y, Ye Y, Yu H, Wang F-Y (2022) Explanation guided cross-modal social image clustering. Inform Sci 593:1\u201316","journal-title":"Inform Sci"},{"key":"16551_CR19","doi-asserted-by":"crossref","unstructured":"Lin Y, Gou Y, Liu X, Bai J, Lv J, Peng X (2022) Dual contrastive prediction for incomplete multi-view representation learning. IEEE Trans Patterna Mach Intell","DOI":"10.1109\/TPAMI.2022.3197238"},{"key":"16551_CR20","doi-asserted-by":"crossref","unstructured":"Bucilu\u01ce C, Caruana R, Niculescu-Mizil A (2006) Model compression. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 535\u2013541","DOI":"10.1145\/1150402.1150464"},{"key":"16551_CR21","doi-asserted-by":"crossref","unstructured":"Zhang Y, Xiang T, Hospedales TM, Lu H (2018) Deep mutual learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4320\u20134328","DOI":"10.1109\/CVPR.2018.00454"},{"key":"16551_CR22","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.inffus.2019.10.006","volume":"56","author":"X Yan","year":"2020","unstructured":"Yan X, Ye Y, Qiu X, Yu H (2020) Synergetic information bottleneck for joint multi-view and ensemble clustering. Inform Fus 56:15\u201327","journal-title":"Inform Fus"},{"key":"16551_CR23","unstructured":"Ravi S, Larochelle H (2016) Optimization as a model for few-shot learning"},{"key":"16551_CR24","unstructured":"Nichol A, Achiam J, Schulman J (2018) On first-order meta-learning algorithms. arXivpreprint arXiv:1803.02999"},{"key":"16551_CR25","unstructured":"Lee Y, Choi S (2018) Gradient-based metalearning with learned layerwise metric and subspace. In: International Conference on Machine Learning, pp. 2927\u20132936. PMLR"},{"key":"16551_CR26","unstructured":"Grant E, Finn C, Levine S, Darrell T, Griffiths T (2018) Recasting gradient-based metalearning as hierarchical bayes. arXiv preprint arXiv:1801.08930"},{"key":"16551_CR27","unstructured":"Zhang R, Che T, Ghahramani Z, Bengio Y, Song Y (2018) Metagan: An adversarial approach to few-shot learning. Adv Neur Inform Proc Syst 31"},{"key":"16551_CR28","unstructured":"Rusu AA, Rao D, Sygnowski J, Vinyals O, Pascanu R, Osindero S, Hadsell R (2018) Meta-learning with latent embedding optimization. arXiv preprint arXiv:1807.05960"},{"key":"16551_CR29","unstructured":"Jiang X, Havaei M, Varno F, Chartrand G, Chapados N, Matwin S (2018) Learning to learn with conditional class dependencies. In: International Conference on Learning Representations"},{"key":"16551_CR30","doi-asserted-by":"crossref","unstructured":"Widhianingsih TDA, Kang D-K (2021) Augmented domain agreement for adaptable meta-learner on few-shot classification. Appl Intell 1\u201317","DOI":"10.1007\/s10489-021-02744-1"},{"key":"16551_CR31","unstructured":"Koch G, Zemel R, Salakhutdinov R et al (2015) Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop, vol. 2, p. 0. Lille"},{"key":"16551_CR32","unstructured":"Vinyals O, Blundell C, Lillicrap T, Wierstra D et al (2016) Matching networks for one shot learning. Adv Neur Inform Proc Syst 29"},{"key":"16551_CR33","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 Conference on Computer Vision and Pattern Recognition, pp. 1199\u20131208","DOI":"10.1109\/CVPR.2018.00131"},{"key":"16551_CR34","unstructured":"Oreshkin B, Rodr\u00edguez L\u00f3pez P, Lacoste A (2018) Tadam: Task dependent adaptive metric for improved few-shot learning. Adv Neur Inform Proc Syst 31"},{"key":"16551_CR35","doi-asserted-by":"publisher","first-page":"8642","DOI":"10.1609\/aaai.v33i01.33018642","volume":"33","author":"W Li","year":"2019","unstructured":"Li W, Xu J, Huo J, Wang L, Gao Y, Luo J (2019) Distribution consistency based covariance metric networks for few-shot learning. Proceedings of the AAAI Conference on Artificial Intelligence 33:8642\u20138649","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"16551_CR36","unstructured":"Yoon SW, Seo J, Moon J (2019) Tapnet: Neural network augmented with task-adaptive projection for few-shot learning. In: International Conference on Machine Learning, pp. 7115\u20137123. PMLR"},{"key":"16551_CR37","unstructured":"Requeima J, Gordon J, Bronskill J, Nowozin S, Turner RE (2019) Fast and flexible multi-task classification using conditional neural adaptive processes. Adv Neur Inform Proc Syst 32"},{"key":"16551_CR38","doi-asserted-by":"crossref","unstructured":"Zhang C, Cai Y, Lin G, Shen C (2020) Deepemd: Few-shot image classification with differentiable earth mover\u2019s distance and structured classifiers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12203\u201312213","DOI":"10.1109\/CVPR42600.2020.01222"},{"key":"16551_CR39","doi-asserted-by":"crossref","unstructured":"Kim J, Kim T, Kim S, Yoo CD (2019) Edgelabeling graph neural network for few-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11\u201320","DOI":"10.1109\/CVPR.2019.00010"},{"key":"16551_CR40","unstructured":"Liu Y, Lee J, Park M, Kim S, Yang E, Hwang SJ, Yang Y (2018) Learning to propagate labels: Transductive propagation network for few-shot learning. arXiv preprint arXiv:1805.10002"},{"key":"16551_CR41","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: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13390\u201313399","DOI":"10.1109\/CVPR42600.2020.01340"},{"key":"16551_CR42","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: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2329\u20132339","DOI":"10.1109\/CVPR46437.2021.00236"},{"key":"16551_CR43","unstructured":"Romero A, Ballas N, Kahou SE, Chassang A, Gatta C, Bengio Y (2014) Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550"},{"key":"16551_CR44","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neur Inform Proc Syst 25"},{"key":"16551_CR45","doi-asserted-by":"crossref","unstructured":"Guo Q, Wang X, Wu Y, Yu Z, Liang D, Hu X, Luo P (2020) Online knowledge distillation via collaborative learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR42600.2020.01103"},{"key":"16551_CR46","doi-asserted-by":"crossref","unstructured":"Liu Y, Cao J, Li B, Yuan C, Hu W, Li Y, Duan Y (2019) Knowledge distillation via instance relationship graph. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR.2019.00726"},{"key":"16551_CR47","unstructured":"Zhou B, Lapedriza A, Xiao J, Torralba A, Oliva A (2014) Learning deep features for scene recognition using places database. Adv Neur Inform Proc Syst 27"},{"key":"16551_CR48","doi-asserted-by":"crossref","unstructured":"Park W, Kim W, You K, Cho M (2020) Diversified mutual learning for deep metric learning. In: European Conference on Computer Vision, pp. 709\u2013725. Springer","DOI":"10.1007\/978-3-030-66415-2_49"},{"key":"16551_CR49","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. Springer","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"16551_CR50","doi-asserted-by":"crossref","unstructured":"Zhou Z, Qiu X, Xie J, Wu J, Zhang C (2021) Binocular mutual learning for improving few-shot classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 8402\u20138411","DOI":"10.1109\/ICCV48922.2021.00829"},{"key":"16551_CR51","unstructured":"Wang X, Zhang R, Sun Y, Qi J (2018) Kdgan: Knowledge distillation with generative adversarial networks. Adv Neur Inform Proc Syst 31"},{"key":"16551_CR52","doi-asserted-by":"crossref","unstructured":"Liu Y, Chen K, Liu C, Qin Z, Luo Z, Wang J (2019) Structured knowledge distillation for semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2604\u20132613","DOI":"10.1109\/CVPR.2019.00271"},{"key":"16551_CR53","unstructured":"Minoofam SAH, Bastanfard A, Keyvanpour MR (2021) Trcla: A transfer learning approach to reduce negative transfer for cellular learning automata. IEEE Trans Neur Netw Learn Sys"},{"key":"16551_CR54","doi-asserted-by":"crossref","unstructured":"Gori M, Monfardini G, Scarselli F (2005) A new model for learning in graph domains. In: Proceedings. 2005 IEEE International Joint Conference on Neural Networks, vol. 2, pp. 729\u2013734","DOI":"10.1109\/IJCNN.2005.1555942"},{"key":"16551_CR55","doi-asserted-by":"crossref","unstructured":"Arandjelovic R, Gronat P, Torii A, Pajdla T, Sivic J (2016) Netvlad: Cnn architecture for weakly supervised place recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5297\u20135307","DOI":"10.1109\/CVPR.2016.572"},{"key":"16551_CR56","doi-asserted-by":"crossref","unstructured":"Ma J, Xie H, Han G, Chang S-F, Galstyan A, Abd-Almageed W (2021) Partnerassisted learning for few-shot image classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 10573\u201310582","DOI":"10.1109\/ICCV48922.2021.01040"},{"key":"16551_CR57","doi-asserted-by":"crossref","unstructured":"Schroff F, Kalenichenko D, Philbin J (2015) Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"16551_CR58","doi-asserted-by":"crossref","unstructured":"Kumar V, Glaude H, de Lichy C, Campbell W (2019) A closer look at feature space data augmentation for few-shot intent classification. arXiv preprint arXiv:1910.04176","DOI":"10.18653\/v1\/D19-6101"},{"key":"16551_CR59","doi-asserted-by":"crossref","unstructured":"Kang D, Kwon H, Min J, Cho M (2021) Relational embedding for few-shot classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8822\u20138833","DOI":"10.1109\/ICCV48922.2021.00870"},{"key":"16551_CR60","doi-asserted-by":"crossref","unstructured":"Wertheimer D, Tang L, Hariharan B (2021) Few-shot classification with feature map reconstruction networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8012\u20138021","DOI":"10.1109\/CVPR46437.2021.00792"},{"key":"16551_CR61","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":"16551_CR62","unstructured":"Tseng H-Y, Lee H-Y, Huang J-B, Yang M-H (2020) Cross-domain few-shot classification via learned feature-wise transformation. arXiv preprint arXiv:2001.08735"},{"key":"16551_CR63","unstructured":"Wang Y, Chao W-L, Weinberger KQ, van der Maaten L (2019) Simpleshot: Revisiting nearest-neighbor classification for few-shot learning. arXiv preprint arXiv:1911.04623"},{"key":"16551_CR64","unstructured":"Ziko I, Dolz J, Granger E, Ayed IB (2020) Laplacian regularized few-shot learning. In: International Conference on Machine Learning, pp. 11660\u201311670. PMLR"},{"key":"16551_CR65","unstructured":"Hu SX, Moreno PG, Xiao Y, Shen X, Obozinski G, Lawrence ND, Damianou A (2020) Empirical bayes transductive metalearning with synthetic gradients. arXiv preprint arXiv:2004.12696"},{"key":"16551_CR66","doi-asserted-by":"crossref","unstructured":"Zhao Y, Cheung N-M (2023) Fs-ban: Born-again networks for domain generalization few-shot classification. IEEE Trans Image Proc","DOI":"10.1109\/TIP.2023.3266172"},{"key":"16551_CR67","unstructured":"Ren M, Triantafillou E, Ravi S, Snell J, Swersky K, Tenenbaum JB, Larochelle H, Zemel RS (2018) Meta-learning for semisupervised few-shot classification. arXiv preprint arXiv:1803.00676"},{"key":"16551_CR68","unstructured":"Wah C, Branson S, Welinder P, Perona P, Belongie S (2011) The caltech-ucsd birds-200-2011 dataset"},{"key":"16551_CR69","doi-asserted-by":"crossref","unstructured":"Krause J, Stark M, Deng J, Fei-Fei L (2013) 3d object representations for fine-grained categorization. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 554\u2013561","DOI":"10.1109\/ICCVW.2013.77"},{"issue":"6","key":"16551_CR70","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1109\/TPAMI.2017.2723009","volume":"40","author":"B Zhou","year":"2017","unstructured":"Zhou B, Lapedriza A, Khosla A, Oliva A, Torralba A (2017) Places: A 10 million image database for scene recognition. IEEE Trans Pattern Anal Mach Intell 40(6):1452\u20131464","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"16551_CR71","doi-asserted-by":"crossref","unstructured":"Van Horn G, Mac Aodha O, Song Y, Cui Y, Sun C, Shepard A, Adam H, Perona P, Belongie S (2018) The inaturalist species classification and detection dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8769\u20138778","DOI":"10.1109\/CVPR.2018.00914"},{"key":"16551_CR72","doi-asserted-by":"crossref","unstructured":"Das R, Wang Y-X, Moura JM (2021) On the importance of distractors for fewshot classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9030\u20139040","DOI":"10.1109\/ICCV48922.2021.00890"},{"key":"16551_CR73","first-page":"2734","volume":"33","author":"Z Yue","year":"2020","unstructured":"Yue Z, Zhang H, Sun Q, Hua X-S (2020) Interventional few-shot learning. Adv Neur Inform Proc Syst 33:2734\u20132746","journal-title":"Adv Neur Inform Proc Syst"},{"key":"16551_CR74","unstructured":"Chen W-Y, Liu Y-C, Kira Z, Wang Y-CF, Huang J-B (2019) A closer look at few-shot classification. arXiv preprint arXiv:1904.04232"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16551-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16551-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16551-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T10:46:52Z","timestamp":1709203612000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16551-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,31]]},"references-count":74,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["16551"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16551-y","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2023,8,31]]},"assertion":[{"value":"8 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}