{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:25:50Z","timestamp":1750220750753,"version":"3.41.0"},"reference-count":58,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2020,2,29]],"date-time":"2020-02-29T00:00:00Z","timestamp":1582934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["NO.U1736123 and NO.61572450"],"award-info":[{"award-number":["NO.U1736123 and NO.61572450"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"USTC Research Funds of the Double First-Class Initiative","award":["YD2350002001"],"award-info":[{"award-number":["YD2350002001"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2020,2,29]]},"abstract":"<jats:p>\n            Face hallucination is a domain-specific super-resolution (SR) problem of learning a mapping between a low-resolution (LR) face image and its corresponding high-resolution (HR) image. Tremendous progress on deep learning has shown exciting potential for a variety of face hallucination tasks. However, most deep-learning\u2013based methods are limited to handle facial appearance information without paying attention to facial structure priors. In this article, we propose an open source\n            <jats:sup>1<\/jats:sup>\n            Boundary-aware Dual-branch Network (BDN) for face hallucination, which simultaneously extracts face features and estimates facial boundary responses from LR inputs, ultimately fusing them to reconstruct HR results. Specifically, we first upsample LR face images to HR feature maps, and then feed the upsampled HR features into a memory unit and an attention unit synchronously to obtain the refined features and predict facial boundary responses. Next, they are fed into a feature map fusion unit to combine facial appearance and structure information by a spatial attention mechanism. Moreover, we employ a series of stacked units to boost performance before recovering HR face images. Finally, a discriminative network is developed to improve visual quality by introducing adversarial learning strategy. Extensive experiments show that the proposed approach achieves superior face hallucination results against the state-of-the-art ones.\n          <\/jats:p>","DOI":"10.1145\/3377874","type":"journal-article","created":{"date-parts":[[2020,3,4]],"date-time":"2020-03-04T10:23:32Z","timestamp":1583317412000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["A Deep Learning Approach for Face Hallucination Guided by Facial Boundary Responses"],"prefix":"10.1145","volume":"16","author":[{"given":"Mengyan","family":"Li","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Anhui, China"}]},{"given":"Zhaoyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Anhui, China"}]},{"given":"Guochen","family":"Xie","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Anhui, China"}]},{"given":"Jun","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Anhui, China"}]}],"member":"320","published-online":{"date-parts":[[2020,3,4]]},"reference":[{"volume-title":"Proceedings of the International Conference on Machine Learning. 214--223","year":"2017","author":"Arjovsky Martin","key":"e_1_2_1_1_1"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/AFGR.2000.840616"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2002.1033210"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.116"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00019"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.180"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00264"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00916"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2765202"},{"key":"e_1_2_1_10_1","first-page":"154","article-title":"Theoretical neuroscience: Computational and mathematical modeling of neural systems","volume":"15","author":"Dayan Peter","year":"2001","journal-title":"Journal of Cognitive Neuroscience"},{"volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.","year":"2019","author":"Dogan Berk","key":"e_1_2_1_11_1"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2439281"},{"volume-title":"Proceedings of the International Conference on Advances in Neural Information Processing Systems. 2672--2680","year":"2014","author":"Goodfellow Ian","key":"e_1_2_1_13_1"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4700--4708","author":"Huang Gao","key":"e_1_2_1_15_1"},{"volume-title":"Proceedings of the Workshop on Faces in \u201cReal-Life\u201d Images: Detection, Alignment, and Recognition.","year":"2008","author":"Huang Gary B.","key":"e_1_2_1_16_1"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.187"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00112"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073659"},{"volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1125--1134","author":"Isola Phillip","key":"e_1_2_1_20_1"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.182"},{"volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4876--4884","author":"Kolouri Soheil","key":"e_1_2_1_23_1"},{"key":"e_1_2_1_24_1","first-page":"72","article-title":"Efficient video encoding for automatic video analysis in distributed wireless surveillance systems","volume":"14","author":"Kong Lingchao","year":"2018","journal-title":"ACM Trans. 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