{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T09:58:24Z","timestamp":1782554304559,"version":"3.54.5"},"reference-count":69,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"General Research Fund of HK","award":["27208720"],"award-info":[{"award-number":["27208720"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tip.2021.3131033","type":"journal-article","created":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T21:17:01Z","timestamp":1638998221000},"page":"1120-1133","source":"Crossref","is-referenced-by-count":9,"title":["MetaCloth: Learning Unseen Tasks of Dense Fashion Landmark Detection From a Few Samples"],"prefix":"10.1109","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5818-2589","authenticated-orcid":false,"given":"Yuying","family":"Ge","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9511-7532","authenticated-orcid":false,"given":"Ruimao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6685-7950","authenticated-orcid":false,"given":"Ping","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.124"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00548"},{"key":"ref3","article-title":"LGVTON: A landmark guided approach to virtual try-on","volume-title":"arXiv:2004.00562","author":"Roy","year":"2020"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00838"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01665"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00049"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413832"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00929"},{"key":"ref9","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","volume":"70","author":"Finn"},{"key":"ref10","article-title":"On first-order meta-learning algorithms","volume-title":"arXiv:1803.02999","author":"Nichol","year":"2018"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00011"},{"key":"ref12","article-title":"Meta-learning with adaptive hyperparameters","volume-title":"arXiv:2011.00209","author":"Baik","year":"2020"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00851"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.01002"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2922438"},{"key":"ref16","first-page":"6","article-title":"Few-shot semantic segmentation with prototype learning","volume-title":"Proc. BMVC","volume":"1","author":"Dong"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.2992433"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2021.03.036"},{"key":"ref19","article-title":"Meta-dataset: A dataset of datasets for learning to learn from few examples","volume-title":"arXiv:1903.03096","author":"Triantafillou","year":"2019"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.214"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2929257"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00449"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2019.00146"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00305"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.16"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.328"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00947"},{"key":"ref28","first-page":"2845","article-title":"Delta-encoder: An effective sample synthesis method for few-shot object recognition","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Schwartz"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10599-4_52"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00543"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00353"},{"key":"ref32","article-title":"Improving one-shot learning through fusing side information","volume-title":"arXiv:1710.08347","author":"Hubert Tsai","year":"2017"},{"key":"ref33","first-page":"975","article-title":"Low-shot learning via covariance-preserving adversarial augmentation networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Gao"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.5244\/C.29.37"},{"key":"ref35","first-page":"165","article-title":"Label efficient learning of transferable representations acrosss domains and tasks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Luo"},{"key":"ref36","first-page":"6670","article-title":"Few-shot adversarial domain adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Motiian"},{"key":"ref37","first-page":"2104","article-title":"One-shot unsupervised cross domain translation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Benaim"},{"key":"ref38","first-page":"449","article-title":"Object classification from a single example utilizing class relevance metrics","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"17","author":"Fink"},{"key":"ref39","first-page":"1","article-title":"Siamese neural networks for one-shot image recognition","volume-title":"Proc. Deep Learn. Workshop (ICML)","volume":"2","author":"Koch"},{"key":"ref40","first-page":"2255","article-title":"Few-shot learning through an information retrieval lens","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Triantafillou"},{"key":"ref41","first-page":"721","article-title":"TADAM: Task dependent adaptive metric for improved few-shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Oreshkin"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.569"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_46"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00429"},{"key":"ref45","article-title":"Adaptive posterior learning: Few-shot learning with a surprise-based memory module","volume-title":"arXiv:1902.02527","author":"Ramalho","year":"2019"},{"key":"ref46","first-page":"195","article-title":"One-shot learning with a hierarchical nonparametric Bayesian model","volume-title":"Proc. Workshop Unsupervised Transf. Learn. (ICML)","author":"Salakhutdinov"},{"key":"ref47","article-title":"One-shot generalization in deep generative models","volume-title":"arXiv:1603.05106","author":"Jimenez Rezende","year":"2016"},{"key":"ref48","article-title":"Few-shot autoregressive density estimation: Towards learning to learn distributions","volume-title":"arXiv:1710.10304","author":"Reed","year":"2017"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.14711\/thesis-991012636368103412"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.5244\/C.31.167"},{"key":"ref51","article-title":"Meta-SGD: Learning to learn quickly for few-shot learning","volume-title":"arXiv:1707.09835","author":"Li","year":"2017"},{"key":"ref52","first-page":"113","article-title":"Meta-learning with implicit gradients","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Rajeswaran"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/INDICON49873.2020.9342070"},{"key":"ref54","first-page":"3630","article-title":"Matching networks for one shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Vinyals"},{"key":"ref55","first-page":"4077","article-title":"Prototypical networks for few-shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Snell"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref57","first-page":"523","article-title":"Learning feed-forward one-shot learners","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Bertinetto"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00459"},{"key":"ref59","article-title":"HyperNetworks","volume-title":"arXiv:1609.09106","author":"Ha","year":"2016"},{"key":"ref60","first-page":"7032","article-title":"Learning to model the tail","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01176"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01237-3_27"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_37"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00755"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref67","article-title":"Real-time fashion-guided clothing semantic parsing: A lightweight multi-scale inception neural network and benchmark","volume-title":"Proc. Workshops 31st AAAI Conf. Artif. Intell.","author":"He"},{"key":"ref68","article-title":"Rapid learning or feature reuse? Towards understanding the effectiveness of MAML","volume-title":"arXiv:1909.09157","author":"Raghu","year":"2019"},{"key":"ref69","article-title":"BOIL: Towards representation change for few-shot learning","volume-title":"arXiv:2008.08882","author":"Oh","year":"2020"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/83\/9626658\/09642430.pdf?arnumber=9642430","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T20:53:07Z","timestamp":1709326387000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9642430\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":69,"URL":"https:\/\/doi.org\/10.1109\/tip.2021.3131033","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}