{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:54:59Z","timestamp":1777654499326,"version":"3.51.4"},"reference-count":66,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"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":["62076195"],"award-info":[{"award-number":["62076195"]}],"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":["62206271"],"award-info":[{"award-number":["62206271"]}],"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":["U20B2052"],"award-info":[{"award-number":["U20B2052"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["AUGA5710011522"],"award-info":[{"award-number":["AUGA5710011522"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Key Technical Projects","award":["JSGG20220831105801004"],"award-info":[{"award-number":["JSGG20220831105801004"]}]},{"name":"Shenzhen Key Technical Projects","award":["CJGJZD2022051714160501"],"award-info":[{"award-number":["CJGJZD2022051714160501"]}]},{"name":"Project of Peng Cheng Laboratory","award":["PCL2023A08"],"award-info":[{"award-number":["PCL2023A08"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Artif. Intell."],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1109\/tai.2024.3524977","type":"journal-article","created":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T14:49:22Z","timestamp":1735915762000},"page":"1373-1385","source":"Crossref","is-referenced-by-count":5,"title":["Prompt Customization for Continual Learning"],"prefix":"10.1109","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2137-4821","authenticated-orcid":false,"given":"Yong","family":"Dai","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0611-0636","authenticated-orcid":false,"given":"Xiaopeng","family":"Hong","sequence":"additional","affiliation":[{"name":"Faculty of Computing, Harbin Institute of Technology, Harbin, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2931-572X","authenticated-orcid":false,"given":"Yabin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Xi’an Jiaotong University, Xiaan, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0034-2065","authenticated-orcid":false,"given":"Zhiheng","family":"Ma","sequence":"additional","affiliation":[{"name":"Shenzhen University of Advanced Technology, Shenzhen, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6238-8499","authenticated-orcid":false,"given":"Dongmei","family":"Jiang","sequence":"additional","affiliation":[{"name":"Peng Cheng Laboratory, Shenzhen, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2197-9038","authenticated-orcid":false,"given":"Yaowei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computing, Harbin Institute of Technology, Shenzhen, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2024.3355879"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01754"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02319"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2023.3339091"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01088"},{"key":"ref6","first-page":"14161","article-title":"Instance-aware dynamic prompt tuning for pre-trained point cloud models supplementary file","volume-title":"Proc. IEEE Int. Conf. Comput. Vis.","author":"Zha","year":"2023"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3073436"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2018.2863438"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_9"},{"key":"ref10","first-page":"3987","article-title":"Continual learning through synaptic intelligence","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Zenke","year":"2017"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58621-8_23"},{"key":"ref12","first-page":"16131","article-title":"DualNet: Continual learning, fast and slow","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Pham","year":"2021"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9341460"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00360"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3178874"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2024.3371349"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00024"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19809-0_36"},{"key":"ref19","first-page":"5682","article-title":"S-Prompts learning with pre-trained transformers: An occam\u2019s razor for domain incremental learning","volume-title":"Proc. Conf. Workshop Neural Inf. Process. Syst.","author":"Wang","year":"2022"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01146"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01055"},{"key":"ref22","article-title":"Learning multiple layers of features from tiny images","volume-title":"Handbook Systemic Autoimmune Diseases","author":"Krizhevsky","year":"2009"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref24","first-page":"17","article-title":"CORe50: A new dataset and benchmark for continuous object recognition","volume-title":"Proc. Conf. Robot Learn.","author":"Lomonaco","year":"2017"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"},{"key":"ref26","first-page":"4548","article-title":"Overcoming catastrophic forgetting with hard attention to the task","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Serra","year":"2018"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00810"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00073"},{"key":"ref29","first-page":"18493","article-title":"Continual learning of a mixed sequence of similar and dissimilar tasks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Ke","year":"2020"},{"key":"ref30","first-page":"3925","article-title":"Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Li","year":"2019"},{"key":"ref31","article-title":"Progressive neural networks","author":"Rusu","year":"2016"},{"key":"ref32","article-title":"Lifelong learning with dynamically expandable networks","author":"Yoon","year":"2017"},{"key":"ref33","first-page":"12669","article-title":"Random path selection for continual learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Rajasegaran","year":"2019"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00303"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00697"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2773081"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"ref38","article-title":"DualHSIC: HSIC-bottleneck and alignment for continual learning","author":"Wang","year":"2023"},{"key":"ref39","article-title":"On tiny episodic memories in continual learning","author":"Chaudhry","year":"2019"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00046"},{"key":"ref41","first-page":"15920","article-title":"Dark experience for general continual learning: A strong, simple baseline","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Buzzega","year":"2020"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00938"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_31"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-73021-4_12"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2022.3233791"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2024.3405404"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813687"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19827-4_41"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.353"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01631"},{"key":"ref52","article-title":"Prompt generation networks for efficient adaptation of frozen vision transformers","author":"Loedeman","year":"2022"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01653-1"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/3560815"},{"key":"ref55","article-title":"Visual prompting: Modifying pixel space to adapt pre-trained models","author":"Bahng","year":"2022"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00907"},{"key":"ref57","article-title":"Unsupervised prompt learning for vision-language models","author":"Huang","year":"2022"},{"key":"ref58","first-page":"1195","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Tarvainen","year":"2017"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1037\/\/0033-295x.102.3.419"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.tics.2016.05.004"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26216"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00940"},{"key":"ref64","first-page":"6470","article-title":"Gradient episodic memory for continual learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Lopez-Paz","year":"2017"},{"key":"ref65","article-title":"Encoders and ensembles for task-free continual learning","author":"Shanahan","year":"2021"},{"issue":"11","key":"ref66","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["IEEE Transactions on Artificial Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/9078688\/10980621\/10820551.pdf?arnumber=10820551","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T19:01:20Z","timestamp":1764270080000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10820551\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":66,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tai.2024.3524977","relation":{},"ISSN":["2691-4581"],"issn-type":[{"value":"2691-4581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5]]}}}