{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T16:55:09Z","timestamp":1784134509827,"version":"3.55.0"},"reference-count":38,"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-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/access.2023.3291405","type":"journal-article","created":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T18:20:53Z","timestamp":1688408453000},"page":"71528-71541","source":"Crossref","is-referenced-by-count":52,"title":["On the Role of ViT and CNN in Semantic Communications: Analysis and Prototype Validation"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6263-2516","authenticated-orcid":false,"given":"Hanju","family":"Yoo","sequence":"first","affiliation":[{"name":"School of Integrated Technology, Yonsei University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linglong","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4147-4627","authenticated-orcid":false,"given":"Songkuk","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Integrated Technology, Yonsei University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9561-3341","authenticated-orcid":false,"given":"Chan-Byoung","family":"Chae","sequence":"additional","affiliation":[{"name":"School of Integrated Technology, Yonsei University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390294"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00590"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2022.3223408"},{"key":"ref34","first-page":"2148","article-title":"Predicting parameters in deep learning","volume":"26","author":"denil","year":"2013","journal-title":"Proc Adv Neural Inf Process Syst (NeurIPS)"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3087240"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2003.1292216"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2021.3071210"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i1.19884"},{"key":"ref30","article-title":"Layer normalization","author":"ba","year":"2016","journal-title":"arXiv 1607 06450"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCWorkshops53468.2022.9914635"},{"key":"ref33","year":"2022","journal-title":"BPG image format"},{"key":"ref10","article-title":"6G networks: Beyond Shannon towards semantic and goal-oriented communications","volume":"190","author":"strinati","year":"2021","journal-title":"Comput Netw"},{"key":"ref32","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/18.370119"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1948.tb01338.x"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2022.3191354"},{"key":"ref38","first-page":"694","article-title":"Perceptual losses for real-time style transfer and super-resolution","author":"johnson","year":"2016","journal-title":"Proc Eur Conf Comput Vis (ECCV)"},{"key":"ref19","article-title":"On the relationship between self-attention and convolutional layers","author":"cordonnier","year":"2019","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/VTC2022-Fall57202.2022.10012843"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"ref23","first-page":"213","article-title":"End-to-end object detection with transformers","author":"carion","year":"2020","journal-title":"Proc Eur Conf Comput Vis (ECCV)"},{"key":"ref26","article-title":"Benchmarking neural network robustness to common corruptions and perturbations","author":"hendrycks","year":"2019","journal-title":"arXiv 1903 12261"},{"key":"ref25","first-page":"23296","article-title":"Intriguing properties of vision transformers","volume":"34","author":"naseer","year":"2021","journal-title":"Proc Adv Neural Inf Process Syst (NeurIPS)"},{"key":"ref20","article-title":"An image is worth 16&#x00D7;16 words: Transformers for image recognition at scale","author":"dosovitskiy","year":"2021","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref22","first-page":"3965","article-title":"CoAtNet: Marrying convolution and attention for all data sizes","volume":"34","author":"dai","year":"2021","journal-title":"Proc Adv Neural Inf Process Syst (NeurIPS)"},{"key":"ref21","first-page":"6000","article-title":"Attention is all you need","volume":"30","author":"vaswani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst (NeurIPS)"},{"key":"ref28","article-title":"End-to-end optimized image compression","author":"ball\u00e9","year":"2017","journal-title":"Proc Int Conf Learn Representations (ICLR)"},{"key":"ref27","article-title":"How do vision transformers work","author":"park","year":"2022","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref29","first-page":"30392","article-title":"Early convolutions help transformers see better","volume":"34","author":"xiao","year":"2021","journal-title":"Proc Adv Neural Inf Process Syst (NeurIPS)"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2022.3180802"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2020.2987203"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3219871"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2019.2919300"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JSAC.2022.3221853","article-title":"Guest editorial special issue on beyond transmitting bits: Context, semantics, and task-oriented communications","volume":"41","author":"g\u00fcnd\u00fcz","year":"2023","journal-title":"IEEE J Sel Areas Commun"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3036968"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8461983"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10005208\/10171356.pdf?arnumber=10171356","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T18:23:04Z","timestamp":1691432584000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10171356\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":38,"URL":"https:\/\/doi.org\/10.1109\/access.2023.3291405","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}