{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T20:05:30Z","timestamp":1784318730130,"version":"3.55.0"},"reference-count":89,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":["62376070"],"award-info":[{"award-number":["62376070"]}],"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":["62076195"],"award-info":[{"award-number":["62076195"]}],"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"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Multimedia"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/tmm.2026.3668531","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T19:58:59Z","timestamp":1773086339000},"page":"5785-5797","source":"Crossref","is-referenced-by-count":0,"title":["Continual Conceptual Entity Learning for Text-to-Image Generative Models"],"prefix":"10.1109","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2931-572X","authenticated-orcid":false,"given":"Yabin","family":"Wang","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0611-0636","authenticated-orcid":false,"given":"Xiaopeng","family":"Hong","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0034-2065","authenticated-orcid":false,"given":"Zhiheng","family":"Ma","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6518-3130","authenticated-orcid":false,"given":"Zhou","family":"Su","sequence":"additional","affiliation":[{"name":"Xi’an Jiaotong University, Xi’an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinpeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Intelligent Science & Technology Academy Limited of CASIC, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7385-079X","authenticated-orcid":false,"given":"Zhiwu","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Southampton, Southampton, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"16784","article-title":"GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Nichol","year":"2022"},{"key":"ref2","article-title":"Hierarchical text-conditional image generation with clip latents","author":"Ramesh","year":"2022"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00214"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00978"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02155"},{"key":"ref7","article-title":"An image is worth one word: Personalizing text-to-image generation using textual inversion","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Gal","year":"2023"},{"key":"ref8","article-title":"InstantID: Zero-shot identity-preserving generation in seconds","author":"Wang","year":"2024"},{"key":"ref9","first-page":"2990","article-title":"Continual learning with deep generative replay","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Shin","year":"2017"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2023.05.020"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00904"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00285"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.255"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.310"},{"key":"ref15","article-title":"LoRa: Low-rank adaptation of large language models","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hu","year":"2021"},{"key":"ref16","article-title":"LoRA: Using low-rank adaptation to quickly fine-tune diffusion models","year":"2023"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref18","article-title":"P$+$: Extended textual conditioning in text-to-image generation","author":"Voynov","year":"2023"},{"key":"ref19","first-page":"21548","article-title":"CONES: Concept neurons in diffusion models for customized generation","volume-title":"Proc. 40th Int. Conf. Mach. Learn.","volume":"202","author":"Liu","year":"2023"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.568"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.l803.03635"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.629"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01366"},{"key":"ref24","first-page":"30105","article-title":"StyleGAN-T: Unlocking the power of GANs for fast large-scale text-to-image synthesis","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Sauer","year":"2023"},{"key":"ref25","article-title":"Scaling autoregressive models for content-rich text-to-image generation","volume":"2022","author":"Yu","year":"2022","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref26","first-page":"8821","article-title":"Zero-shot text-to-image generation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ramesh","year":"2021"},{"key":"ref27","first-page":"19822","article-title":"CogView: Mastering text-to-image generation via transformers","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Ding","year":"2021"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2024.3382484"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3278992"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2024.3359769"},{"key":"ref31","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Radford","year":"2021"},{"key":"ref32","first-page":"72958","article-title":"Inserting anybody in diffusion models via celeb basis","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Yuan","year":"2023"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01370"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00816"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00624"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00192"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00848"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.52202\/075280-0699"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2025.3633153"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52734.2025.00267"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.eacl-main.239"},{"key":"ref43","article-title":"Adaptive budget allocation for parameter-efficient fine-tuning","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Zhang","year":"2022"},{"key":"ref44","article-title":"IncreLoRa: Incremental parameter allocation method for parameter-efficient fine-tuning","author":"Zhang","year":"2023"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/303"},{"key":"ref46","first-page":"9900","article-title":"Continual learning in low-rank orthogonal subspaces","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Chaudhry","year":"2020"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.587"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2019.01.044"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00092"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00046"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01322"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00088"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58565-5_6"},{"key":"ref54","first-page":"9516","article-title":"Co2L: Contrastive continual learning","volume-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis.","author":"Cha","year":"2021"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19806-9_23"},{"key":"ref56","first-page":"5962","article-title":"Memory replay GANs: Learning to generate new categories without forgetting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Wu","year":"2018"},{"key":"ref57","first-page":"16481","article-title":"GAN memory with no forgetting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Cong","year":"2020"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00121"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00390"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"ref61","first-page":"3987","article-title":"Continual learning through synaptic intelligence","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zenke","year":"2017"},{"key":"ref62","first-page":"22379","article-title":"AFEC: Active forgetting of negative transfer in continual learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Wang","year":"2021"},{"key":"ref63","first-page":"2621","article-title":"Kernel continual learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Derakhshani","year":"2021"},{"key":"ref64","first-page":"4392","article-title":"Uncertainty-based continual learning with adaptive regularization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Ahn","year":"2019"},{"key":"ref65","first-page":"3647","article-title":"Continual learning with node-importance based adaptive group sparse regularization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Jung","year":"2020"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2773081"},{"key":"ref67","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":"ref68","first-page":"4548","article-title":"Overcoming catastrophic forgetting with hard attention to the task","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Serra","year":"2018"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00810"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58621-8_23"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00907"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr46437.2021.00303"},{"key":"ref73","article-title":"DARTS: Differentiable architecture search","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liu","year":"2018"},{"key":"ref74","article-title":"ProxylessNAS: Direct neural architecture search on target task and hardware","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Cai","year":"2018"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00024"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19809-0_36"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0411"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01146"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00181"},{"key":"ref80","article-title":"Continual diffusion: Continual customization of text-to-image diffusion with C-LoRa","volume":"2024","author":"Smith","year":"2024","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref81","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Ho","year":"2020"},{"key":"ref82","article-title":"Denoising diffusion implicit models","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Song","year":"2020"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72684-2_11"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-73232-4_24"},{"key":"ref85","article-title":"Simple-aesthetics-predictor: Clip-based aesthetics predictor inspired by the interface of huggingface transformers","year":"2023"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.52202\/075280-1594"},{"key":"ref87","article-title":"Progressive growing of GANs for improved quality, stability, and variation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Karras","year":"2018"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.11588\/dah.2016.2.23376"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00951"}],"container-title":["IEEE Transactions on Multimedia"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6046\/11342315\/11425818.pdf?arnumber=11425818","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T19:45:48Z","timestamp":1784317548000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11425818\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":89,"URL":"https:\/\/doi.org\/10.1109\/tmm.2026.3668531","relation":{},"ISSN":["1520-9210","1941-0077"],"issn-type":[{"value":"1520-9210","type":"print"},{"value":"1941-0077","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}