{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T14:51:14Z","timestamp":1777733474097,"version":"3.51.4"},"reference-count":93,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"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":["62172041"],"award-info":[{"award-number":["62172041"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176021"],"award-info":[{"award-number":["62176021"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2023,2,1]]},"DOI":"10.1109\/tpami.2022.3164894","type":"journal-article","created":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T19:32:03Z","timestamp":1649187123000},"page":"1545-1562","source":"Crossref","is-referenced-by-count":14,"title":["Curvature-Adaptive Meta-Learning for Fast Adaptation to Manifold Data"],"prefix":"10.1109","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4424-4352","authenticated-orcid":false,"given":"Zhi","family":"Gao","sequence":"first","affiliation":[{"name":"Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology (BIT), Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6300-6336","authenticated-orcid":false,"given":"Yuwei","family":"Wu","sequence":"additional","affiliation":[{"name":"Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology (BIT), Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6937-6300","authenticated-orcid":false,"given":"Mehrtash","family":"Harandi","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Systems Eng., Monash University, Melbourne, VIC, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1900-8945","authenticated-orcid":false,"given":"Yunde","family":"Jia","sequence":"additional","affiliation":[{"name":"Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology (BIT), Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00830"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1515\/9781400830244"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00645"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-02396-5"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2927301"},{"key":"ref59","first-page":"3630","article-title":"Matching networks for one shot learning","author":"vinyals","year":"2016","journal-title":"Proc Neural Informat Process Syst"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00929"},{"key":"ref58","first-page":"818","article-title":"Visualizing and understanding convolutional networks","author":"zeiler","year":"2014","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449872"},{"key":"ref52","first-page":"4868","article-title":"Hyperbolic graph convolutional neural networks","author":"chami","year":"2019","journal-title":"Proc Neural Informat Process Syst"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3003846"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/b98852"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.70735"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00547"},{"key":"ref17","first-page":"5345","article-title":"Hyperbolic neural networks","author":"ganea","year":"2018","journal-title":"Proc Neural Informat Process Syst"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00122"},{"key":"ref19","first-page":"1","article-title":"Learning mixed-curvature representations in product spaces","author":"gu","year":"2019","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref18","first-page":"1","article-title":"Mixed-curvature variational autoencoders","author":"skopek","year":"2020","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref93","first-page":"1","article-title":"A closer look at deep learning heuristics: Learning rate restarts, warmup and distillation","author":"gotmare","year":"2019","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1007\/BF02289565"},{"key":"ref51","first-page":"486","article-title":"Constant curvature graph convolutional networks","author":"bachmann","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2316836"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2012.6386109"},{"key":"ref90","first-page":"1690","article-title":"Conditional neural processes","author":"garnelo","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref46","article-title":"Hyperbolic graph attention network","author":"zhang","year":"2019"},{"key":"ref45","first-page":"8230","article-title":"Hyperbolic graph neural networks","author":"liu","year":"2019","journal-title":"Proc Neural Informat Process Syst"},{"key":"ref89","first-page":"18860","article-title":"Task-robust model-agnostic meta-learning","author":"collins","year":"2020","journal-title":"Proc Neural Informat Process Syst"},{"key":"ref48","first-page":"1419","article-title":"HoroPCA: Hyperbolic dimensionality reduction via horospherical projections","author":"chami","year":"2021","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00022"},{"key":"ref42","first-page":"20 755","article-title":"Meta-learning with adaptive hyperparameters","author":"baik","year":"2020","journal-title":"Proc Neural Informat Process Syst"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"ref41","article-title":"Meta-SGD: Learning to learn quickly for few-shot learning","author":"li","year":"2017"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00236"},{"key":"ref44","first-page":"1","article-title":"Hyperbolic neural networks++","author":"shimizu","year":"2020","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_35"},{"key":"ref43","first-page":"4693","article-title":"A wrapped normal distribution on hyperbolic space for gradient-based learning","author":"nagano","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01069"},{"key":"ref49","first-page":"15065","article-title":"From trees to continuous embeddings and back: Hyperbolic hierarchical clustering","author":"chami","year":"2020","journal-title":"Proc Neural Informat Process Syst"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01537"},{"key":"ref7","first-page":"1842","article-title":"Meta-learning with memory-augmented neural networks","author":"santoro","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3136921"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01098"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00824"},{"key":"ref6","first-page":"741","article-title":"Prototype rectification for few-shot learning","author":"liu","year":"2020","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref5","first-page":"113","article-title":"Meta-learning with implicit gradients","author":"rajeswaran","year":"2019","journal-title":"Proc Int Conf Neural Informat Process Syst"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58558-7_2"},{"key":"ref81","article-title":"Deep meta-learning: Learning to learn in the concept space","author":"zhou","year":"2018"},{"key":"ref40","first-page":"1","article-title":"Optimization as a model for few-shot learning","author":"ravi","year":"2017","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2910052"},{"key":"ref83","first-page":"10457","article-title":"Local propagation for few-shot learning","author":"lifchitz","year":"2020","journal-title":"Proc Int Conf Pattern Recognit"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093338"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00651"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01348"},{"key":"ref34","first-page":"1","article-title":"Towards a neural statistician","author":"edwards","year":"2017","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref78","first-page":"2850","article-title":"Delta-encoder: An effective sample synthesis method for few-shot object recognition","author":"schwartz","year":"2018","journal-title":"Proc Neural Informat Process Syst"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3102098"},{"key":"ref36","first-page":"20865","article-title":"Gradient-em Bayesian meta-learning","author":"zou","year":"2020","journal-title":"Proc Neural Informat Process Syst"},{"key":"ref31","first-page":"10 617","article-title":"MetaFun: Meta-learning with iterative functional updates","author":"xu","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01091"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00419"},{"key":"ref74","first-page":"721","article-title":"Tadam: Task dependent adaptive metric for improved few-shot learning","author":"oreshkin","year":"2018","journal-title":"Proc Neural Informat Process Syst"},{"key":"ref33","first-page":"11409","article-title":"Learning to learn kernels with variational random features","author":"zhen","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref77","first-page":"1","article-title":"Learning to learn with conditional class dependencies","author":"jiang","year":"2019","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref32","first-page":"1","article-title":"A simple neural attentive meta-learner","author":"mishra","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref76","first-page":"2371","article-title":"MetaGAN: An adversarial approach to few-shot learning","author":"zhang","year":"2018","journal-title":"Proc Neural Informat Process Syst"},{"key":"ref2","first-page":"7693","article-title":"Fast context adaptation via meta-learning","author":"zintgraf","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref1","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","author":"finn","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref39","first-page":"3981","article-title":"Learning to learn by gradient descent by gradient descent","author":"andrychowicz","year":"2016","journal-title":"Proc Neural Informat Process Syst"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00933"},{"key":"ref71","article-title":"On first-order meta-learning algorithms","author":"nichol","year":"2018"},{"key":"ref70","first-page":"1","article-title":"Meta-learning with latent embedding optimization","author":"rusu","year":"2019","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref73","first-page":"3661","article-title":"Rapid adaptation with conditionally shifted neurons","author":"munkhdalai","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01199"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00042"},{"key":"ref23","first-page":"4077","article-title":"Prototypical networks for few-shot learning","author":"snell","year":"2017","journal-title":"Proc Neural Informat Process Syst"},{"key":"ref67","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00870"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00883"},{"key":"ref69","first-page":"1","article-title":"How to train your MAML","author":"antoniou","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref20","first-page":"1","article-title":"DeepSphere: Towards an equivariant graph-based spherical CNN","author":"defferrard","year":"2019","journal-title":"Proc Workshop Representation Learn Graphs Manifolds"},{"key":"ref64","first-page":"1","article-title":"Meta-learning for semi-supervised few-shot classification","author":"ren","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.202"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2776154"},{"key":"ref66","first-page":"1","article-title":"Meta-learning with differentiable closed-form solvers","author":"bertinetto","year":"2019","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01228"},{"key":"ref65","article-title":"The caltech-UCSD birds-200&#x2013;2011 dataset","author":"wah","year":"2011"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01450"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01259"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00370"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2723400"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5811"},{"key":"ref61","first-page":"2582","article-title":"Deep CNNs meet global covariance pooling: Better representation and generalization","volume":"43","author":"wang","year":"2021","journal-title":"IEEE Trans Pattern Anal Mach Intell"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10008914\/09749838.pdf?arnumber=9749838","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T19:47:17Z","timestamp":1675108037000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9749838\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,1]]},"references-count":93,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2022.3164894","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,1]]}}}