{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T09:12:41Z","timestamp":1775034761281,"version":"3.50.1"},"reference-count":45,"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:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/access.2026.3676463","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T20:09:20Z","timestamp":1774296560000},"page":"46118-46128","source":"Crossref","is-referenced-by-count":0,"title":["Swin-TUNA: A Novel PEFT Approach for Accurate Food Image Segmentation"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9871-6428","authenticated-orcid":false,"given":"Haotian","family":"Chen","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3187-1629","authenticated-orcid":false,"given":"Zhiyong","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref2","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2021","journal-title":"arXiv:2010.11929"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-68821-9_51"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475201"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3330047"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/s0079-7421(08)60536-8"},{"key":"ref8","article-title":"An empirical investigation of catastrophic forgetting in gradient-based neural networks","volume-title":"Proc. 2nd Int. Conf. Learn. Represent. (ICLR)","author":"Goodfellow"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-short.8"},{"key":"ref10","first-page":"1022","article-title":"Compacter: Efficient low-rank hypercomplex adapter layers","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Mahabadi"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.295"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.158"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.424"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW67362.2025.00647"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2025.3546874"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2025.118794"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfoodeng.2023.111833"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfca.2024.107110"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfoodeng.2024.112134"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00397"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-17916-z"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s00530-025-01669-w"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TASLPRO.2025.3606231"},{"key":"ref25","first-page":"6747","article-title":"Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Shi"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.143"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-short.1"},{"key":"ref28","article-title":"LoRa: Low-rank adaptation of large language models","author":"Hu","year":"2021","journal-title":"arXiv:2106.09685"},{"key":"ref29","first-page":"2790","article-title":"Parameter-efficient transfer learning for NLP","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Houlsby"},{"key":"ref30","first-page":"16664","article-title":"AdaptFormer: Adapting vision transformers for scalable visual recognition","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chen"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.01869"},{"key":"ref32","article-title":"How to alleviate catastrophic forgetting in LLMs finetuning? Hierarchical layer-wise and element-wise regularization","author":"Song","year":"2025","journal-title":"arXiv:2501.13669"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2025.3570310"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1002\/ima.70194"},{"key":"ref35","article-title":"Parameter-efficient fine-tuning for medical image analysis: The missed opportunity","author":"Dutt","year":"2024","journal-title":"arXiv:2305.08252"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref37","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Proc. 3rd Int. Conf. Learn. Represent. (ICLR)","author":"Simonyan"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref40","volume-title":"MMSegmentation: Openmmlab Semantic Segmentation Toolbox and Benchmark","author":"Contributors","year":"2020"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1802.02611"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.3390\/s21227504"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.108380"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3237871"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/11323511\/11450377.pdf?arnumber=11450377","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T06:08:35Z","timestamp":1775023715000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11450377\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":45,"URL":"https:\/\/doi.org\/10.1109\/access.2026.3676463","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}