{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T01:16:17Z","timestamp":1768439777522,"version":"3.49.0"},"reference-count":60,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871016"],"award-info":[{"award-number":["61871016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976060"],"award-info":[{"award-number":["61976060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010226","name":"Department of Education of Guangdong Province","doi-asserted-by":"publisher","award":["2018KCXTD019"],"award-info":[{"award-number":["2018KCXTD019"]}],"id":[{"id":"10.13039\/501100010226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011311","name":"Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China","doi-asserted-by":"publisher","award":["ICT 20061"],"award-info":[{"award-number":["ICT 20061"]}],"id":[{"id":"10.13039\/501100011311","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Circuits Syst. Video Technol."],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1109\/tcsvt.2021.3052785","type":"journal-article","created":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T19:53:29Z","timestamp":1635364409000},"page":"4283-4292","source":"Crossref","is-referenced-by-count":34,"title":["Learning to Adapt With Memory for Probabilistic Few-Shot Learning"],"prefix":"10.1109","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8494-0504","authenticated-orcid":false,"given":"Lei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5664-4751","authenticated-orcid":false,"given":"Liyun","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingjun","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5213-0462","authenticated-orcid":false,"given":"Xiantong","family":"Zhen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","first-page":"1","article-title":"Adaptive posterior learning: Few-shot learning with a surprise-based memory module","author":"ramalho","year":"2019","journal-title":"Proc ICLR"},{"key":"ref38","article-title":"Meta-learning with implicit gradients","author":"rajeswaran","year":"2019","journal-title":"arXiv 1909 04630"},{"key":"ref33","article-title":"A simple neural attentive meta-learner","author":"mishra","year":"2018","journal-title":"arXiv 1707 03141"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2018.2848458"},{"key":"ref31","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref30","article-title":"Photo-realistic image super-resolution via variational autoencoders","author":"liu","year":"2020","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00755"},{"key":"ref36","first-page":"721","article-title":"Tadam: Task dependent adaptive metric for improved few-shot learning","author":"oreshkin","year":"2018","journal-title":"Proc NeurIPS"},{"key":"ref35","article-title":"Rapid adaptation with conditionally shifted neurons","author":"munkhdalai","year":"2017","journal-title":"arXiv 1712 09926"},{"key":"ref34","first-page":"2554","article-title":"Meta networks","author":"munkhdalai","year":"2017","journal-title":"Proc ICML"},{"key":"ref60","first-page":"7693","article-title":"Fast context adaptation via meta-learning","author":"zintgraf","year":"2019","journal-title":"Proc ICML"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01091"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1126\/science.aab3050"},{"key":"ref29","first-page":"1","article-title":"Learning to propagate labels: Transductive propagation network for few-shot learning","author":"liu","year":"2019","journal-title":"Proc ICLR"},{"key":"ref2","article-title":"Meta-learning with differentiable closed-form solvers","author":"bertinetto","year":"2019","journal-title":"arXiv 1805 08136"},{"key":"ref1","article-title":"Infinite mixture prototypes for few-shot learning","author":"allen","year":"2019","journal-title":"arXiv 1902 04552"},{"key":"ref20","article-title":"Learning to remember rare events","author":"kaiser","year":"2017","journal-title":"arXiv 1703 03129"},{"key":"ref22","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref21","article-title":"Attentive neural processes","author":"kim","year":"2019","journal-title":"arXiv 1901 05761"},{"key":"ref24","first-page":"3581","article-title":"Semi-supervised learning with deep generative models","author":"kingma","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref23","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"2013","journal-title":"arXiv 1312 6114"},{"key":"ref26","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref25","first-page":"1","article-title":"Siamese neural networks for one-shot image recognition","author":"koch","year":"2015","journal-title":"Proc ICML Workshop"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref51","author":"thrun","year":"2012","journal-title":"Learning to Learn"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2940647"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00177"},{"key":"ref57","article-title":"Long-term video question answering via multimodal hierarchical memory attentive networks","author":"yu","year":"2020","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00286"},{"key":"ref55","article-title":"Memory networks","author":"weston","year":"2015","journal-title":"arXiv 1410 3916"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2017.2703920"},{"key":"ref53","first-page":"3630","article-title":"Matching networks for one shot learning","author":"vinyals","year":"2016","journal-title":"Proc NIPS"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"ref10","first-page":"1","article-title":"Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm","author":"finn","year":"2018","journal-title":"Proc ICLR"},{"key":"ref11","first-page":"9516","article-title":"Probabilistic model-agnostic meta-learning","author":"finn","year":"2018","journal-title":"Proc NeurIPS"},{"key":"ref40","first-page":"1","article-title":"Optimization as a model for few-shot learning","author":"ravi","year":"2017","journal-title":"Proc ICLR"},{"key":"ref12","article-title":"Few-shot learning with graph neural networks","author":"garcia","year":"2018","journal-title":"arXiv 1711 04043"},{"key":"ref13","first-page":"1690","article-title":"Conditional neural processes","author":"garnelo","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref14","article-title":"Neural processes","author":"garnelo","year":"2018","journal-title":"arXiv 1807 01622"},{"key":"ref15","article-title":"Meta-learning probabilistic inference for prediction","author":"gordon","year":"2019","journal-title":"arXiv 1805 09921"},{"key":"ref16","article-title":"Neural turing machines","author":"graves","year":"2014","journal-title":"arXiv 1410 5401"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1038\/nature20101"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00587"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2020.2995754"},{"key":"ref4","article-title":"A new meta-baseline for few-shot learning","author":"chen","year":"2020","journal-title":"arXiv 2003 04390"},{"key":"ref3","first-page":"523","article-title":"Learning feed-forward one-shot learners","author":"bertinetto","year":"2016","journal-title":"Proc NIPS"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00382"},{"key":"ref5","first-page":"1","article-title":"Reproducing meta-learning with differentiable closed-form solvers","author":"devos","year":"2019","journal-title":"Proc ICLR"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2909427"},{"key":"ref7","article-title":"Towards a neural statistician","author":"edwards","year":"2016","journal-title":"arXiv 1606 02185"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00049"},{"key":"ref9","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","author":"finn","year":"2017","journal-title":"Proc ICML"},{"key":"ref46","first-page":"4077","article-title":"Prototypical networks for few-shot learning","author":"snell","year":"2017","journal-title":"Proc NIPS"},{"key":"ref45","first-page":"1","article-title":"Few-shot learning with graph neural networks","author":"satorras","year":"2018","journal-title":"Proc ICLR"},{"key":"ref48","first-page":"2440","article-title":"End-to-end memory networks","author":"sukhbaatar","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref47","first-page":"3483","article-title":"Learning structured output representation using deep conditional generative models","author":"sohn","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref42","first-page":"1","article-title":"Stochastic backpropagation and approximate inference in deep generative models","author":"rezende","year":"2014","journal-title":"Proc ACM Int Conf Mach Learn"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00042"},{"key":"ref44","first-page":"1842","article-title":"Meta-learning with memory-augmented neural networks","author":"santoro","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"}],"container-title":["IEEE Transactions on Circuits and Systems for Video Technology"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/76\/9590085\/09328494.pdf?arnumber=9328494","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:50:25Z","timestamp":1652194225000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9328494\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11]]},"references-count":60,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tcsvt.2021.3052785","relation":{},"ISSN":["1051-8215","1558-2205"],"issn-type":[{"value":"1051-8215","type":"print"},{"value":"1558-2205","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11]]}}}