{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T15:57:24Z","timestamp":1783526244972,"version":"3.55.0"},"reference-count":53,"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":["62076099"],"award-info":[{"award-number":["62076099"]}],"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":["61703166"],"award-info":[{"award-number":["61703166"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Program of Guangzhou","award":["202103010003"],"award-info":[{"award-number":["202103010003"]}]},{"name":"Key Science and Technology Program of Foshan","award":["2020001006285"],"award-info":[{"award-number":["2020001006285"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Multimedia"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tmm.2022.3198880","type":"journal-article","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T19:49:17Z","timestamp":1660592957000},"page":"5763-5774","source":"Crossref","is-referenced-by-count":20,"title":["Learning Relative Feature Displacement for Few-Shot Open-Set Recognition"],"prefix":"10.1109","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8011-2744","authenticated-orcid":false,"given":"Shule","family":"Deng","sequence":"first","affiliation":[{"name":"School of Automation Science and Engineering, South China University of Technology, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2148-2726","authenticated-orcid":false,"given":"Jin-Gang","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, South China University of Technology, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8001-0708","authenticated-orcid":false,"given":"Zihao","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, South China University of Technology, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongxia","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, South China University of Technology, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8985-3541","authenticated-orcid":false,"given":"Yansheng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8687-4427","authenticated-orcid":false,"given":"Yang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Xi&#x0027;an Jiaotong University, Xi&#x0027;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00974"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01267-0_2"},{"key":"ref15","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume":"70","author":"finn","year":"0","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11774"},{"key":"ref53","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01091"},{"key":"ref11","first-page":"523","article-title":"Learning feed-forward one-shot learners","author":"bertinetto","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref10","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":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.2993952"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.3001510"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01238"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.256"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref50","first-page":"1139","article-title":"On the importance of initialization and momentum in deep learning","volume":"28","author":"sutskever","year":"0","journal-title":"Proc 30th Int Conf Mach Learn"},{"key":"ref46","first-page":"1","article-title":"Meta-learning with differentiable closed-form solvers","author":"bertinetto","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00883"},{"key":"ref48","first-page":"1","article-title":"Optimization as a model for few-shot learning","author":"ravi","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref47","first-page":"1","article-title":"Meta-learning for semi-supervised few-shot classification","author":"ren","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00610"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.17021"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00948"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00870"},{"key":"ref49","article-title":"CIFAR-10 and CIFAR-100 datasets","author":"krizhevsky","year":"2009"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.16"},{"key":"ref7","first-page":"2850","article-title":"Delta-encoder: An effective sample synthesis method for few-shot object recognition","author":"schwartz","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3386252"},{"key":"ref3","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":"ref6","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.6b00367"},{"key":"ref5","first-page":"1","article-title":"Possibilities and challenges for artificial intelligence in military applications","author":"svenmarck","year":"0","journal-title":"Proc NATO Big Data Artif Intell Mil Decis Mak Specialists' Meeting"},{"key":"ref40","first-page":"7115","article-title":"TapNet: Neural network augmented with task-adaptive projection for few-shot learning","volume":"97","author":"yoon","year":"0","journal-title":"Proc 36th Int Conf Mach Learn"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2000.855856"},{"key":"ref34","first-page":"1","article-title":"Siamese neural networks for one-shot image recognition","volume":"2","author":"koch","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref37","first-page":"10852","article-title":"XtarNet: Learning to extract task-adaptive representation for incremental few-shot learning","author":"yoon","year":"0","journal-title":"Proc 37th Int Conf Mach Learn"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00459"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01183"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00414"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58580-8_7"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01349"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2006.79"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00882"},{"key":"ref39","first-page":"719","article-title":"TADAM: Task dependent adaptive metric for improved few-shot learning","author":"oreshkin","year":"0","journal-title":"Proc 32nd Int Conf Neural Inf Process Syst"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00193"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00893"},{"key":"ref23","first-page":"1","article-title":"A closer look at few-shot classification","author":"chen","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref26","first-page":"1","article-title":"A baseline for few-shot image classification","author":"dhillon","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.173"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2981604"},{"key":"ref21","first-page":"9175","article-title":"Reducing network agnostophobia","author":"dhamija","year":"0","journal-title":"Proc 32nd Int Conf Neural Inf Process Syst"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.5244\/C.31.42"},{"key":"ref27","first-page":"1","article-title":"Revisiting fine-tuning for few-shot learning","author":"nakamura","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00241"}],"container-title":["IEEE Transactions on Multimedia"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6046\/10016790\/09857616.pdf?arnumber=9857616","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T20:04:53Z","timestamp":1701115493000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9857616\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":53,"URL":"https:\/\/doi.org\/10.1109\/tmm.2022.3198880","relation":{},"ISSN":["1520-9210","1941-0077"],"issn-type":[{"value":"1520-9210","type":"print"},{"value":"1941-0077","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}