{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T16:53:26Z","timestamp":1767977606425,"version":"3.49.0"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Korean Government","award":["NRF-2023R1A2C1004868"],"award-info":[{"award-number":["NRF-2023R1A2C1004868"]}]},{"name":"Institute of Information and Communications Technology Planning and Evaluation (IITP) under the Metaverse Support Program to Nurture the Best Talents"},{"name":"Korean Government","award":["IITP-2023-RS-2023-00256615"],"award-info":[{"award-number":["IITP-2023-RS-2023-00256615"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3378092","type":"journal-article","created":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T19:21:25Z","timestamp":1710789685000},"page":"57741-57754","source":"Crossref","is-referenced-by-count":7,"title":["Swin Transformer Fusion Network for Image Quality Assessment"],"prefix":"10.1109","volume":"12","author":[{"given":"Hyeongmyeon","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, Konkuk University, Seoul, South Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3826-6395","authenticated-orcid":false,"given":"Changhoon","family":"Yim","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Konkuk University, Seoul, South Korea"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2003.1292216"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2009.2025923"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2010.2092435"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2011.2109730"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2718185"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2013.2293423"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.1995.537485"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/S0141-9382(99)00009-8"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2005.859389"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2005.859378"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3045810"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00194"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/QoMEX.2019.8743252"},{"key":"ref16","first-page":"633","article-title":"PIPAL: A large-scale image quality assessment dataset for perceptual image restoration","volume-title":"Proc. Eur. Conf. Comput. Vis. (ECCV)","author":"Gu"},{"key":"ref17","article-title":"Image quality assessment for perceptual image restoration: A new dataset, benchmark and metric","author":"Gu","year":"2020","journal-title":"arXiv:2011.15002"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2006.881959"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1117\/1.3267105"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2014.10.009"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00123"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2930707"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3040416"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3118295"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3289168"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00109"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2012.2197011"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2018.5496"},{"key":"ref29","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Krizhevsky"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref31","first-page":"1","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","volume-title":"Proc. Int. Conf. Learn. Represent. (ICIR)","author":"Dosovitskiy"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref34","article-title":"Layer normalization","author":"Lei Ba","year":"2016","journal-title":"arXiv:1607.06450"},{"key":"ref35","first-page":"807","article-title":"ReLU: Rectified linear units improve restricted Boltzmann machines","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Nair"},{"key":"ref36","first-page":"1","article-title":"Mish: A self-regularized non-monotonic activation function","volume-title":"Proc. Brit. Mach. Vis. Conf. (BMVC)","author":"Misra"},{"key":"ref37","volume-title":"The Kendall Rank Correlation Coefficient","author":"Abdi","year":"2024"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-00296-0_5"},{"key":"ref39","first-page":"1","article-title":"Einops: Clear and reliable tensor manipulations with einstein-like notation","volume-title":"Proc. Int. Conf. Learn. Represent. (ICIR)","author":"Rogozhnikov"},{"key":"ref40","volume-title":"PyTorch Toolbox for Image Quality Assessment","year":"2023"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10380310\/10473038.pdf?arnumber=10473038","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T19:01:01Z","timestamp":1714762861000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10473038\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3378092","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}