{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T20:22:47Z","timestamp":1740169367926,"version":"3.37.3"},"reference-count":50,"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\/"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000028","name":"Center for Co-Design of Cognitive Systems (CoCoSys), one of the seven centers in Joint University Microelectronics Program 2.0 (JUMP 2.0), a Semiconductor Research Corporation (SRC) Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000028","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000185","name":"Defense Advanced Research Projects Agency","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]},{"name":"SRC"},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100002418","name":"Intel Corporation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100002418","id-type":"DOI","asserted-by":"publisher"}]},{"name":"DoD Vannevar Bush Fellowship"},{"DOI":"10.13039\/100006754","name":"U.S. Army Research Laboratory","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006754","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3397061","type":"journal-article","created":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T17:40:54Z","timestamp":1715017254000},"page":"64360-64375","source":"Crossref","is-referenced-by-count":0,"title":["Toward Visual Syntactical Understanding"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7463-8396","authenticated-orcid":false,"given":"Sayeed Shafayet","family":"Chowdhury","sequence":"first","affiliation":[{"name":"Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA"}]},{"given":"Soumyadeep","family":"Chandra","sequence":"additional","affiliation":[{"name":"Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0735-9695","authenticated-orcid":false,"given":"Kaushik","family":"Roy","sequence":"additional","affiliation":[{"name":"Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA"}]}],"member":"263","reference":[{"volume-title":"Syntax Vs Semantics","year":"2021","key":"ref1"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1979.4766899"},{"volume-title":"Syntactic Methods in Pattern Recognition","year":"1974","author":"Fu","key":"ref3"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/800192.805762"},{"key":"ref5","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Krizhevsky"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.375"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1151"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref10","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018","journal-title":"arXiv:1810.04805"},{"key":"ref11","article-title":"What do you learn from context? Probing for sentence structure in contextualized word representations","author":"Tenney","year":"2019","journal-title":"arXiv:1905.06316"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1179"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1202"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00321"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.290"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1561\/0600000018"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/BF00208719"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780198536710.001.0001"},{"key":"ref19","article-title":"BEiT: BERT pre-training of image transformers","author":"Bao","year":"2021","journal-title":"arXiv:2106.08254"},{"key":"ref20","first-page":"1","article-title":"Support vector method for novelty detection","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"12","author":"Sch\u00f6lkopf"},{"key":"ref21","first-page":"4393","article-title":"Deep one-class classification","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ruff"},{"key":"ref22","first-page":"1100","article-title":"Deep structured energy based models for anomaly detection","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"48","author":"Zhai"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20893-6_39"},{"key":"ref25","article-title":"Anomaly detection with generative adversarial networks for multivariate time series","author":"Li","year":"2018","journal-title":"arXiv:1809.04758"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177704472"},{"key":"ref27","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"139","author":"Radford"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00988"},{"key":"ref29","first-page":"1","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Ren"},{"issue":"8","key":"ref30","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI Blog"},{"key":"ref31","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":"ref32","first-page":"11","article-title":"Large-scale celebfaces attributes (CelebA) dataset","volume":"15","author":"Liu","year":"2018","journal-title":"Retrieved August"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00821"},{"volume-title":"Balanced Accuracy","year":"2023","key":"ref34"},{"key":"ref35","first-page":"1","article-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Lee"},{"key":"ref36","article-title":"ClipCap: CLIP prefix for image captioning","author":"Mokady","year":"2021","journal-title":"arXiv:2111.09734"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01954"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00294"},{"key":"ref39","first-page":"1","article-title":"Deep anomaly detection using geometric transformations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Golan"},{"key":"ref40","article-title":"Probabilistic modeling of deep features for out-of-distribution and adversarial detection","author":"Ahuja","year":"2019","journal-title":"arXiv:1909.11786"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i12.26720"},{"key":"ref42","first-page":"1","article-title":"Deep autoencoding Gaussian mixture model for unsupervised anomaly detection","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zong"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3293772"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-21735-7_7"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00624"},{"issue":"11","key":"ref46","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298990"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.330"},{"volume-title":"Thinking, Fast and Slow","year":"2011","author":"Kahneman","key":"ref49"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2022.806397"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/6287639\/10380310\/10520274-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10380310\/10520274.pdf?arnumber=10520274","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T17:30:34Z","timestamp":1715362234000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10520274\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3397061","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2024]]}}}