{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T15:35:48Z","timestamp":1782833748335,"version":"3.54.5"},"reference-count":63,"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":["62206166"],"award-info":[{"award-number":["62206166"]}],"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":["61991410"],"award-info":[{"award-number":["61991410"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Multimedia"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tmm.2022.3215310","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T20:55:57Z","timestamp":1667508957000},"page":"6881-6891","source":"Crossref","is-referenced-by-count":31,"title":["An Adversarial Meta-Training Framework for Cross-Domain Few-Shot Learning"],"prefix":"10.1109","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5472-2469","authenticated-orcid":false,"given":"Pinzhuo","family":"Tian","sequence":"first","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8016-9310","authenticated-orcid":false,"given":"Shaorong","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3386252"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3058303"},{"key":"ref3","first-page":"5331","article-title":"Efficient off-policy meta-reinforcement learning via probabilistic context variables","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Rakelly","year":"2019"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1253"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3165715"},{"key":"ref6","first-page":"1","article-title":"Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Finn","year":"2018"},{"key":"ref7","first-page":"1","article-title":"Cross-domain few-shot classification via learned feature-wise transformation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Tseng","year":"2020"},{"key":"ref8","article-title":"Self-training for few-shot transfer across extreme task differences","author":"Phoo","year":"2020"},{"key":"ref9","article-title":"Explain and improve: Cross-domain few-shot-learning using explanations","author":"Sun","year":"2020"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/149"},{"key":"ref11","first-page":"1","article-title":"BOIL: Towards Representation Change for Few-Shot Learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Oh","year":"2021"},{"key":"ref12","first-page":"5250","article-title":"Learning where to learn: Gradient sparsity in meta and continual learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Oswald","year":"2021"},{"key":"ref13","article-title":"A simple neural attentive meta-learner","author":"Mishra","year":"2017"},{"key":"ref14","first-page":"2554","article-title":"Meta networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Munkhdalai","year":"2017"},{"key":"ref15","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Finn","year":"2017"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00760"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00888"},{"key":"ref18","first-page":"8152","article-title":"Data augmentation for meta-learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ni","year":"2021"},{"key":"ref19","first-page":"11887","article-title":"Improving generalization in meta-learning via task augmentation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yao","year":"2021"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICME46284.2020.9102917"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00783"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107946"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3079209"},{"key":"ref24","first-page":"3630","article-title":"Matching networks for one shot learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Vinyals","year":"2016"},{"key":"ref25","first-page":"4077","article-title":"Prototypical networks for few-shot learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Snell","year":"2017"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107797"},{"key":"ref28","first-page":"1","article-title":"Optimization as a model for few-shot learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ravi","year":"2017"},{"key":"ref29","first-page":"1842","article-title":"Meta-learning with memory-augmented neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Santoro","year":"2016"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108662"},{"key":"ref31","first-page":"1","article-title":"Meta-learning with differentiable closed-form solvers","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Bertinetto","year":"2019"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01091"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"ref34","first-page":"1","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Raghu","year":"2020"},{"key":"ref35","first-page":"1","article-title":"How to train your MAML","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Antoniou","year":"2019"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-7687-1_79"},{"key":"ref37","first-page":"2672","article-title":"Generative adversarial nets","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Goodfellow","year":"2014"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413561"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58583-9_8"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-007-0176-2"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2765202"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2019.2921336"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11671"},{"key":"ref44","first-page":"719","article-title":"TADAM: Task dependent adaptive metric for improved few-shot learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Oreshkin","year":"2018"},{"key":"ref45","article-title":"Certifiable distributional robustness with principled adversarial training","volume":"2","author":"Sinha","year":"2017"},{"key":"ref46","first-page":"5339","article-title":"Generalizing to unseen domains via adversarial data augmentation","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Volpi","year":"2018"},{"key":"ref47","first-page":"14435","article-title":"Maximum-entropy adversarial data augmentation for improved generalization and robustness","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhao","year":"2020"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2723009"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2019.2918242"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2016.01419"},{"key":"ref51","article-title":"Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (ISIC","author":"Codella","year":"2019"},{"key":"ref52","first-page":"5250","article-title":"Caltech-ucsd birds 200","author":"Welinder","year":"2010"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2013.6706807"},{"key":"ref55","article-title":"Fine-grained visual classification of aircraft","author":"Maji","year":"2013"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2013.77"},{"key":"ref57","first-page":"1","article-title":"Meta-dataset: A dataset of datasets for learning to learn from few examples","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Triantafillou","year":"2019"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01450"},{"key":"ref59","first-page":"1","article-title":"A universal representation transformer layer for few-shot image classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liu","year":"2020"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00834"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00939"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref63","first-page":"719","article-title":"Tadam: Task dependent adaptive metric for improved few-shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Oreshkin","year":"2018"}],"container-title":["IEEE Transactions on Multimedia"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6046\/10016790\/09921329.pdf?arnumber=9921329","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T22:56:57Z","timestamp":1705964217000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9921329\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":63,"URL":"https:\/\/doi.org\/10.1109\/tmm.2022.3215310","relation":{},"ISSN":["1520-9210","1941-0077"],"issn-type":[{"value":"1520-9210","type":"print"},{"value":"1941-0077","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}