{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T06:56:39Z","timestamp":1780556199318,"version":"3.54.1"},"reference-count":38,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,30]]},"DOI":"10.1109\/sibgrapi62404.2024.10716316","type":"proceedings-article","created":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T17:27:39Z","timestamp":1729272459000},"page":"1-6","source":"Crossref","is-referenced-by-count":8,"title":["Multi-Feature Aggregation in Diffusion Models for Enhanced Face Super-Resolution"],"prefix":"10.1109","author":[{"given":"Marcelo","family":"dos Santos","sequence":"first","affiliation":[{"name":"Federal University of Parana,Curitiba,Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rayson","family":"Laroca","sequence":"additional","affiliation":[{"name":"Federal University of Parana,Curitiba,Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rafael O.","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"Brazilian Federal Police,Brasilia,Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jo\u00e3o C.","family":"Neves","sequence":"additional","affiliation":[{"name":"University of Beira Interior,Covilha,Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Menotti","sequence":"additional","affiliation":[{"name":"Federal University of Parana,Curitiba,Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2002.1033210"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3485132"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46454-1_37"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/sibgrapi55357.2022.9991753"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00101"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.5220\/0012398900003660"},{"key":"ref7","first-page":"36479","article-title":"Photorealistic text-to-image diffusion models with deep language understanding","volume":"35","author":"Saharia","year":"2022","journal-title":"International Conf on Neural Infor-mation Processing Systems (NeurIPS)"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.30420\/456164054"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00589"},{"key":"ref10","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","volume-title":"International Conference on Machine Learning","author":"Sohl-Dickstein","year":"2015"},{"key":"ref11","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"International Conference on Neural Information Processing Systems (NeurIPS)","volume":"33","author":"Ho","year":"2020"},{"key":"ref12","first-page":"1","article-title":"Generative modeling by estimating gradients of the data distribution","volume-title":"International Conference on Neural Information Processing Systems (NeurIPS)","author":"Song","year":"2019"},{"key":"ref13","first-page":"12438","article-title":"Improved techniques for training score-based generative models","volume":"33","author":"Song","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref14","first-page":"1","article-title":"Score-based generative modeling through stochastic differential equations","volume-title":"International Conference on Learning Rep-resentations (ICLR)","author":"Song","year":"May 2021"},{"key":"ref15","first-page":"4474","article-title":"Permutation invariant graph generation via score-based generative modeling","volume-title":"International Conference on Artificial Intelli-gence and Statistics (AISTATS)","volume":"108","author":"Niu","year":"2020"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58580-8_22"},{"key":"ref17","first-page":"1","article-title":"Solving inverse problems in medical imaging with score-based generative models","volume-title":"International Conference on Learning Representations (ICLR)","author":"Song","year":"2022"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00584"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3204461"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.01.029"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/SIBGRAPI55357.2022.9991799"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00622"},{"key":"ref23","article-title":"Gotta go fast when generating data with score-based models","author":"Jolicoeur-Martineau","year":"2021","journal-title":"arXiv preprint"},{"key":"ref24","first-page":"11287","article-title":"Score-based generative modeling in latent space","volume-title":"International Conference on Neural Information Processing Systems (NeurIPS)","volume":"34","author":"Vahdat","year":"2021"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01374"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4149(82)90051-5"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-12616-5"},{"key":"ref28","doi-asserted-by":"crossref","DOI":"10.1017\/9781108186735","volume-title":"Applied stochastic differential equations","volume":"10","author":"Sarkka","year":"2019"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1162\/NECO_a_00142"},{"key":"ref30","first-page":"8162","article-title":"Improved denoising diffusion probabilis-tic models","volume-title":"International Conference on Machine Learning (ICML)","author":"Nichol","year":"2021"},{"key":"ref31","article-title":"AssemblyAI","volume-title":"How Imagen Actually Works","year":"2023"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1049\/iet-bmt.2016.0178"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01819"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref37","article-title":"Learning face representation from scratch","author":"Yi","year":"2014","journal-title":"arXiv preprint"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00966"}],"event":{"name":"2024 37th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)","location":"Manaus, Brazil","start":{"date-parts":[[2024,9,30]]},"end":{"date-parts":[[2024,10,3]]}},"container-title":["2024 37th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10716261\/10716262\/10716316.pdf?arnumber=10716316","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,19]],"date-time":"2024-10-19T04:53:31Z","timestamp":1729313611000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10716316\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,30]]},"references-count":38,"URL":"https:\/\/doi.org\/10.1109\/sibgrapi62404.2024.10716316","relation":{},"subject":[],"published":{"date-parts":[[2024,9,30]]}}}