{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T17:03:40Z","timestamp":1782407020669,"version":"3.54.5"},"reference-count":108,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106282"],"award-info":[{"award-number":["62106282"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"AMS Equipment Development Research Fund","award":["ZXD2020C2316"],"award-info":[{"award-number":["ZXD2020C2316"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Multimedia"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tmm.2022.3142955","type":"journal-article","created":{"date-parts":[[2022,1,13]],"date-time":"2022-01-13T20:31:00Z","timestamp":1642105860000},"page":"2111-2126","source":"Crossref","is-referenced-by-count":46,"title":["Multi-Level Second-Order Few-Shot Learning"],"prefix":"10.1109","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0734-0078","authenticated-orcid":false,"given":"Hongguang","family":"Zhang","sequence":"first","affiliation":[{"name":"Systems Engineering Institute, AMS, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4125-1554","authenticated-orcid":false,"given":"Hongdong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Engineering and Computer Science, Australian National University, Canberra, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6340-5289","authenticated-orcid":false,"given":"Piotr","family":"Koniusz","sequence":"additional","affiliation":[{"name":"College of Engineering and Computer Science, Australian National University, Canberra, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3077729"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00053"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58555-6_1"},{"key":"ref58","article-title":"Covariance tracker","author":"porikli","year":"0","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref53","article-title":"On first-order meta-learning algorithms","author":"nichol","year":"2018"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2901464"},{"key":"ref55","first-page":"721","article-title":"Tadam: Task dependent adaptive metric for improved few-shot learning","author":"oreshkin","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00047"},{"key":"ref50","article-title":"Learning to propagate labels: Transductive propagation network for few-shot learning","author":"liu","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01091"},{"key":"ref45","article-title":"One shot learning of simple visual concepts","volume":"33","author":"lake","year":"0","journal-title":"Proc Annu Meeting Cogn Sci Soc"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_38"},{"key":"ref47","article-title":"Meta-SGD: Learning to learn quickly for few shot learning","author":"li","year":"2017"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00605"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3107164"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2013.77"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.170"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2012.2236306"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33786-4_32"},{"key":"ref9","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","author":"chen","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref4","article-title":"A theoretical analysis of feature pooling in vision algorithms","volume":"28","author":"boureau","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10599-4_29"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_9"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539963"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58558-7_31"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01376-1"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2012.10.010"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.755"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_3"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3107160"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01270-0_48"},{"key":"ref31","article-title":"Predict then propagate: Graph neural networks meet personalized pagerank","author":"klicpera","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref30","article-title":"Semi-supervised classification with graph convolutional networks","author":"thomas kipf","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6247939"},{"key":"ref32","article-title":"Siamese neural networks for one-shot image recognition","volume":"2","author":"koch","year":"0","journal-title":"Proc ICML Deep Learn Workshop"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2545667"},{"key":"ref38","article-title":"Higher-order occurrence pooling on mid- and low-level features: Visual concept detection","author":"koniusz","year":"2013"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.3001510"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.3039329"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206609"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01199"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.404"},{"key":"ref21","article-title":"Unsupervised learning via meta-learning","author":"hsu","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref28","article-title":"Novel dataset for fine-grained image categorization","author":"khosla","year":"0","journal-title":"Proc 1st Workshop Fine-Grained Vis Categorization IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref27","first-page":"10132","article-title":"Unsupervised meta-learning for few-shot image classification","volume":"32","author":"khodadadeh","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00010"},{"key":"ref13","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","author":"finn","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref12","first-page":"449","article-title":"Object classification from a single example utilizing class relevance metrics","author":"fink","year":"0","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00459"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00815"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2019.00131"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01222"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00288"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2006.79"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00931"},{"key":"ref10","article-title":"Adversarial feature learning","author":"donahue","year":"2016"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2013.2252622"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00011"},{"key":"ref19","first-page":"1404","article-title":"Joint dimensionality reduction and metric learning: A geometric take","author":"harandi","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_40"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1145\/3206025.3206028"},{"key":"ref92","first-page":"6861","article-title":"Simplifying graph convolutional networks","author":"wu","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00177"},{"key":"ref94","article-title":"Flat: Few-shot learning via autoencoding transformation regularizers","author":"xu","year":"2019"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1037\/h0074898"},{"key":"ref90","first-page":"1473","article-title":"Distance metric learning for large margin nearest neighbor classification","author":"kilian","year":"0","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref89","article-title":"SimpleShot: Revisiting nearest-neighbor classification for few-shot learning","author":"wang","year":"2019"},{"key":"ref86","article-title":"3D skeleton-based few-shot action recognition with JEANIE is not so naïve","author":"wang","year":"2021"},{"key":"ref85","article-title":"The Caltech-UCSD Birds-200-2011 dataset","author":"wah","year":"2011"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-47426-3_5"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2010.2056366"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557109"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2928540"},{"key":"ref84","first-page":"3637","article-title":"Matching networks for one shot learning","author":"vinyals","year":"0","journal-title":"Proc 30th Int Conf Neural Inf Process Syst"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1007\/11744047_45"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1145\/2998574"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref108","first-page":"7693","article-title":"Fast context adaptation via meta-learning","author":"zintgraf","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/152"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.2993952"},{"key":"ref75","article-title":"UCF101: A dataset of 101 human actions classes from videos in the wild","author":"soomro","year":"2012"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.3031062"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.3002177"},{"key":"ref105","first-page":"751","article-title":"Compound memory networks for few-shot video classification","author":"zhu","year":"0","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref77","first-page":"465","article-title":"Fisher-bures adversary graph convolutional networks","author":"sun","year":"0","journal-title":"Proc Conf Uncertainty of Artificial Intelligence"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58571-6_38"},{"key":"ref103","article-title":"Simple spectral graph convolution","author":"zhu","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.117"},{"key":"ref1","article-title":"How to train your maml","author":"antoniou","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978942"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2942030"},{"key":"ref73","first-page":"4080","article-title":"Prototypical networks for few-shot learning","author":"snell","year":"0","journal-title":"Proc 31st Int Conf Neural Inf Process Syst"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58598-3_33"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.772"},{"key":"ref67","article-title":"Few-shot learning with graph neural networks","author":"satorras","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1145\/2733373.2806216"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1145\/2466715.2466733"},{"key":"ref66","first-page":"4974","article-title":"A simple neural network module for relational reasoning","author":"santoro","year":"0","journal-title":"Proc 31st Int Conf Neural Inf Process Syst"},{"key":"ref65","article-title":"Meta-learning with latent embedding optimization","author":"rusu","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-39935-3"},{"key":"ref62","article-title":"Meta-learning for semi-supervised few-shot classification","author":"ren","year":"0","journal-title":"Proc 6th Int Conf Learn Representations"},{"key":"ref61","first-page":"5275","article-title":"Incremental few-shot learning with attention attractor networks","volume":"32","author":"ren","year":"2019","journal-title":"Adv Neural Inf Process Syst"}],"container-title":["IEEE Transactions on Multimedia"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6046\/10016790\/09681205.pdf?arnumber=9681205","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T18:46:08Z","timestamp":1687805168000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9681205\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":108,"URL":"https:\/\/doi.org\/10.1109\/tmm.2022.3142955","relation":{},"ISSN":["1520-9210","1941-0077"],"issn-type":[{"value":"1520-9210","type":"print"},{"value":"1941-0077","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}