{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T05:45:22Z","timestamp":1748843122390,"version":"3.37.3"},"reference-count":72,"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:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/access.2023.3332488","type":"journal-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T19:28:08Z","timestamp":1699903688000},"page":"128724-128735","source":"Crossref","is-referenced-by-count":3,"title":["SQ-Swin: Siamese Quadratic Swin Transformer for Lettuce Browning Prediction"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0531-2110","authenticated-orcid":false,"given":"Dayang","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Massachusetts at Lowell, Lowell, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2095-1095","authenticated-orcid":false,"given":"Boce","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Food Science and Human Nutrition, University of Florida, Gainesville, FL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongshun","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Massachusetts at Lowell, Lowell, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6164-4318","authenticated-orcid":false,"given":"Yaguang","family":"Luo","sequence":"additional","affiliation":[{"name":"United States Department of Agriculture, Food Quality Laboratory\/Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, Beltsville, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5852-0813","authenticated-orcid":false,"given":"Hengyong","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Massachusetts at Lowell, Lowell, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2212\/spr.2007.6.16","article-title":"Enzymatic browning and its control in fresh-cut produce","volume":"3","author":"He","year":"2007","journal-title":"Stewart Postharvest Rev."},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1016\/j.foodres.2004.05.005"},{"doi-asserted-by":"publisher","key":"ref3","DOI":"10.3390\/ijms18020377"},{"doi-asserted-by":"publisher","key":"ref4","DOI":"10.1021\/jf000644q"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1016\/j.postharvbio.2016.05.001"},{"doi-asserted-by":"publisher","key":"ref6","DOI":"10.1109\/CVPR.2016.90"},{"doi-asserted-by":"publisher","key":"ref7","DOI":"10.1109\/TAI.2021.3128132"},{"doi-asserted-by":"publisher","key":"ref8","DOI":"10.1088\/1361-6560\/aba87c"},{"doi-asserted-by":"publisher","key":"ref9","DOI":"10.1109\/IJCNN48605.2020.9207296"},{"key":"ref10","article-title":"Manifoldron: Direct space partition via manifold discovery","author":"Wang","year":"2022","journal-title":"arXiv:2201.05279"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.1109\/TransAI49837.2020.00016"},{"doi-asserted-by":"publisher","key":"ref12","DOI":"10.1016\/j.bios.2021.113209"},{"key":"ref13","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018","journal-title":"arXiv:1810.04805"},{"doi-asserted-by":"publisher","key":"ref14","DOI":"10.18653\/v1\/2020.acl-main.204"},{"doi-asserted-by":"publisher","key":"ref15","DOI":"10.18653\/v1\/2020.acl-demos.30"},{"key":"ref16","article-title":"Language models are few-shot learners","author":"Brown","year":"2020","journal-title":"arXiv:2005.14165"},{"doi-asserted-by":"publisher","key":"ref17","DOI":"10.1016\/j.media.2022.102615"},{"doi-asserted-by":"publisher","key":"ref18","DOI":"10.3390\/app12010468"},{"key":"ref19","article-title":"Transformers in medical image analysis: A review","author":"He","year":"2022","journal-title":"arXiv:2202.12165"},{"doi-asserted-by":"publisher","key":"ref20","DOI":"10.1016\/j.media.2023.102802"},{"doi-asserted-by":"publisher","key":"ref21","DOI":"10.1109\/MIPR54900.2022.00048"},{"doi-asserted-by":"publisher","key":"ref22","DOI":"10.1016\/j.media.2020.101838"},{"key":"ref23","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020","journal-title":"arXiv:2010.11929"},{"key":"ref24","article-title":"CTformer: Convolution-free token2token dilated vision transformer for low-dose CT denoising","author":"Wang","year":"2022","journal-title":"arXiv:2202.13517"},{"doi-asserted-by":"publisher","key":"ref25","DOI":"10.1109\/CVPR.2009.5206848"},{"doi-asserted-by":"publisher","key":"ref26","DOI":"10.1109\/ICCV48922.2021.00986"},{"doi-asserted-by":"publisher","key":"ref27","DOI":"10.1109\/TMI.2019.2963248"},{"key":"ref28","first-page":"13445","article-title":"Optimization and generalization of shallow neural networks with quadratic activation functions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Sarao Mannelli"},{"doi-asserted-by":"publisher","key":"ref29","DOI":"10.1016\/j.neunet.2020.01.007"},{"key":"ref30","article-title":"Robust generalization of quadratic neural networks via function identification","author":"Xu","year":"2021","journal-title":"arXiv:2109.10935"},{"doi-asserted-by":"publisher","key":"ref31","DOI":"10.1109\/tnnls.2023.3331380"},{"key":"ref32","first-page":"1","article-title":"Siamese neural networks for one-shot image recognition","volume-title":"Proc. ICML Deep Learn. Workshop","volume":"2","author":"Koch"},{"doi-asserted-by":"publisher","key":"ref33","DOI":"10.1007\/978-3-319-48881-3_56"},{"doi-asserted-by":"publisher","key":"ref34","DOI":"10.1007\/978-1-0716-0826-5_3"},{"doi-asserted-by":"publisher","key":"ref35","DOI":"10.1109\/CVPR46437.2021.01549"},{"doi-asserted-by":"publisher","key":"ref36","DOI":"10.1109\/CVPR.2018.00508"},{"doi-asserted-by":"publisher","key":"ref37","DOI":"10.1109\/CVPR42600.2020.00630"},{"doi-asserted-by":"publisher","key":"ref38","DOI":"10.1109\/IGARSS46834.2022.9883139"},{"doi-asserted-by":"publisher","key":"ref39","DOI":"10.1109\/CVPR.2017.106"},{"doi-asserted-by":"publisher","key":"ref40","DOI":"10.1109\/CVPR.2019.00656"},{"doi-asserted-by":"publisher","key":"ref41","DOI":"10.1109\/TPAMI.2019.2938758"},{"doi-asserted-by":"publisher","key":"ref42","DOI":"10.1109\/CVPR42600.2020.01261"},{"doi-asserted-by":"publisher","key":"ref43","DOI":"10.1111\/1467-8659.00675"},{"doi-asserted-by":"publisher","key":"ref44","DOI":"10.1109\/ICCV.2019.00502"},{"key":"ref45","article-title":"ImageNet-21K pretraining for the masses","author":"Ridnik","year":"2021","journal-title":"arXiv:2104.10972"},{"key":"ref46","first-page":"3833","article-title":"Rethinking pre-training and self-training","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Zoph"},{"key":"ref47","article-title":"On first-order meta-learning algorithms","author":"Nichol","year":"2018","journal-title":"arXiv:1803.02999"},{"doi-asserted-by":"publisher","key":"ref48","DOI":"10.1016\/j.postharvbio.2019.110931"},{"doi-asserted-by":"publisher","key":"ref49","DOI":"10.1016\/j.postharvbio.2021.111653"},{"doi-asserted-by":"publisher","key":"ref50","DOI":"10.1016\/j.patcog.2017.10.009"},{"key":"ref51","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Paszke"},{"doi-asserted-by":"publisher","key":"ref52","DOI":"10.1007\/978-3-642-00296-0_5"},{"key":"ref53","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv:1409.1556"},{"doi-asserted-by":"publisher","key":"ref54","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref55","first-page":"9355","article-title":"Twins: Revisiting the design of spatial attention in vision transformers","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Chu"},{"key":"ref56","first-page":"10347","article-title":"Training data-efficient image transformers & distillation through attention","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Touvron"},{"key":"ref57","first-page":"15908","article-title":"Transformer in transformer","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Han"},{"doi-asserted-by":"publisher","key":"ref58","DOI":"10.1109\/ICCV48922.2021.00063"},{"volume-title":"PyTorch Image Models","year":"2019","author":"Wightman","key":"ref59"},{"doi-asserted-by":"publisher","key":"ref60","DOI":"10.1016\/j.postharvbio.2021.111626"},{"doi-asserted-by":"publisher","key":"ref61","DOI":"10.1007\/978-3-319-10602-1_48"},{"doi-asserted-by":"publisher","key":"ref62","DOI":"10.1109\/ICIRD.2018.8376299"},{"doi-asserted-by":"publisher","key":"ref63","DOI":"10.1109\/CVPR42600.2020.01164"},{"issue":"1","key":"ref64","first-page":"31","article-title":"Video streaming in online learning","volume":"14","author":"Hartsell","year":"2006","journal-title":"AACE Rev., Formerly AACE J."},{"key":"ref65","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Ho"},{"key":"ref66","article-title":"LLaMA: Open and efficient foundation language models","author":"Touvron","year":"2023","journal-title":"arXiv:2302.13971"},{"doi-asserted-by":"publisher","key":"ref67","DOI":"10.1109\/ICCV.2017.74"},{"doi-asserted-by":"publisher","key":"ref68","DOI":"10.18653\/v1\/N16-3020"},{"key":"ref69","first-page":"1","article-title":"A unified approach to interpreting model predictions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Lundberg"},{"doi-asserted-by":"publisher","key":"ref70","DOI":"10.1007\/978-1-4842-9178-8_2"},{"doi-asserted-by":"publisher","key":"ref71","DOI":"10.1016\/0168-9002(94)00931-7"},{"doi-asserted-by":"publisher","key":"ref72","DOI":"10.3390\/s21217241"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10005208\/10316294.pdf?arnumber=10316294","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T18:18:23Z","timestamp":1709403503000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10316294\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":72,"URL":"https:\/\/doi.org\/10.1109\/access.2023.3332488","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2023]]}}}