{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T16:45:54Z","timestamp":1793205954726,"version":"4.5.0"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/tpami.2020.3007511","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T16:24:54Z","timestamp":1594139094000},"page":"1-1","source":"Crossref","is-referenced-by-count":79,"title":["Label Independent Memory for Semi-Supervised Few-shot Video Classification"],"prefix":"10.1109","author":[{"given":"Linchao","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"3637","article-title":"Matching networks for one shot learning","volume-title":"Proc. 30th Int. Conf. Neural Inf. Process. Syst.","author":"Vinyals"},{"key":"ref2","first-page":"4080","article-title":"Prototypical networks for few-shot learning","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Snell"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref4","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Finn"},{"key":"ref5","article-title":"Learning to remember rare events","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kaiser"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_46"},{"key":"ref7","article-title":"Unsupervised learning via meta-learning","author":"Hsu","year":"2019"},{"key":"ref8","article-title":"YouTube-8M: A large-scale video classification benchmark","author":"Abu-El-Haija","year":"2016"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref10","article-title":"Memory networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Weston"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref12","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Simonyan"},{"key":"ref13","first-page":"568","article-title":"Two-stream convolutional networks for action recognition in videos","volume-title":"Proc. 27th Int. Conf. Neural Inf. Process. Syst.","author":"Simonyan"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-017-1013-y"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.515"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.441"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46484-8_2"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.510"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.502"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.572"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.337"},{"key":"ref22","article-title":"Learnable pooling with context gating for video classification","author":"Miech","year":"2017"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.147"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00877"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00675"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01267-0_19"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2000.855856"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2006.79"},{"key":"ref29","first-page":"2568","article-title":"One shot learning of simple visual concepts","volume-title":"Proc. 33rd Annu. Meeting Cogn. Sci. Soc.","author":"Lake"},{"key":"ref30","first-page":"1842","article-title":"Meta-learning with memory-augmented neural networks","volume-title":"Proc. 33rd Int. Conf. Mach. Learn.","author":"Santoro"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01063"},{"key":"ref32","article-title":"Temporal ensembling for semi-supervised learning","author":"Laine"},{"key":"ref33","first-page":"1195","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Tarvainen"},{"key":"ref34","first-page":"5049","article-title":"MixMatch: A holistic approach to semi-supervised learning","volume-title":"Proc. 33rd Int. Conf. Neural Inf. Process. Syst.","author":"Berthelot"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/springerreference_178963"},{"key":"ref36","article-title":"Meta-learning for semi-supervised few-shot classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ren"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP40778.2020.9191037"},{"key":"ref38","first-page":"10276","article-title":"Learning to self-train for semi-supervised few-shot classification","volume-title":"Proc. 33rd Int. Conf. Neural Inf. Process. Syst.","author":"Li"},{"key":"ref39","first-page":"6000","article-title":"Attention is all you need","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Vaswani"},{"key":"ref40","article-title":"A structured self-attentive sentence embedding","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Lin"},{"key":"ref41","first-page":"1578","article-title":"All about VLAD","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Arandjelovic"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5540039"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00459"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref45","article-title":"The kinetics human action video dataset","author":"Kay","year":"2017"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.622"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-038"},{"key":"ref49","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma"},{"key":"ref50","first-page":"265","article-title":"TensorFlow: A system for large-scale machine learning","volume-title":"Proc. 12th USENIX Conf. Operating Syst. Des. Implementation","author":"Abadi"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/4359286\/09134951.pdf?arnumber=9134951","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T17:35:41Z","timestamp":1704821741000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9134951\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/tpami.2020.3007511","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":[[2020]]}}}