{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T03:00:14Z","timestamp":1776567614909,"version":"3.51.2"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:00:00Z","timestamp":1736380800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:00:00Z","timestamp":1736380800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2018AAA0103203"],"award-info":[{"award-number":["2018AAA0103203"]}]},{"name":"Sichuan Science and Technology Program","award":["2021JDRC0005"],"award-info":[{"award-number":["2021JDRC0005"]}]},{"DOI":"10.13039\/501100019014","name":"Chengdu Science and Technology Project","doi-asserted-by":"crossref","award":["2022-YF05-02014-SN"],"award-info":[{"award-number":["2022-YF05-02014-SN"]}],"id":[{"id":"10.13039\/501100019014","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s40747-024-01762-z","type":"journal-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T08:40:39Z","timestamp":1736412039000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A joint learning method for low-light facial expression recognition"],"prefix":"10.1007","volume":"11","author":[{"given":"Yuanlun","family":"Xie","sequence":"first","affiliation":[]},{"given":"Jie","family":"Ou","sequence":"additional","affiliation":[]},{"given":"Bihan","family":"Wen","sequence":"additional","affiliation":[]},{"given":"Zitong","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Wenhong","family":"Tian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,9]]},"reference":[{"key":"1762_CR1","doi-asserted-by":"crossref","unstructured":"Li S, Deng W (2020) Deep facial expression recognition: a survey. IEEE Trans Affect Comput 1\u20131","DOI":"10.11834\/jig.200233"},{"issue":"5","key":"1762_CR2","doi-asserted-by":"publisher","first-page":"2439","DOI":"10.1109\/TIP.2018.2886767","volume":"28","author":"Y Li","year":"2018","unstructured":"Li Y, Zeng J, Shan S, Chen X (2018) Occlusion aware facial expression recognition using cnn with attention mechanism. IEEE Trans Image Process 28(5):2439\u20132450","journal-title":"IEEE Trans Image Process"},{"key":"1762_CR3","doi-asserted-by":"publisher","first-page":"4057","DOI":"10.1109\/TIP.2019.2956143","volume":"29","author":"K Wang","year":"2020","unstructured":"Wang K, Peng X, Yang J, Meng D, Qiao Y (2020) Region attention networks for pose and occlusion robust facial expression recognition. IEEE Trans Image Process 29:4057\u20134069","journal-title":"IEEE Trans Image Process"},{"issue":"5","key":"1762_CR4","doi-asserted-by":"publisher","first-page":"3190","DOI":"10.1109\/TCSVT.2021.3103782","volume":"32","author":"Y Li","year":"2021","unstructured":"Li Y, Gao Y, Chen B, Zhang Z, Lu G, Zhang D (2021) Self-supervised exclusive-inclusive interactive learning for multi-label facial expression recognition in the wild. IEEE Trans Circuits Syst Video Technol 32(5):3190\u20133202","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"1762_CR5","doi-asserted-by":"crossref","unstructured":"Zhao Z, Liu Q, Zhou F (2021), Robust lightweight facial expression recognition network with label distribution training. In: Proceedings of the AAAI conference on artificial intelligence 35, 3510\u20133519","DOI":"10.1609\/aaai.v35i4.16465"},{"key":"1762_CR6","doi-asserted-by":"crossref","unstructured":"She J, Hu Y, Shi H, Wang J, Shen Q, Mei T (2021), Dive into ambiguity: latent distribution mining and pairwise uncertainty estimation for facial expression recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 6248\u20136257","DOI":"10.1109\/CVPR46437.2021.00618"},{"key":"1762_CR7","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016), Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1762_CR8","doi-asserted-by":"crossref","unstructured":"Liu Z, Mao H, Wu C.-Y, Feichtenhofer C, Darrell T, Xie S (2022), A convnet for the 2020s. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 11976\u201311986","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"1762_CR9","doi-asserted-by":"crossref","unstructured":"Li Z, Zheng J (2016), Single image brightening via exposure fusion. In: 2016 IEEE International conference on acoustics, speech and signal processing (ICASSP). IEEE, 1756\u20131760","DOI":"10.1109\/ICASSP.2016.7471978"},{"issue":"3","key":"1762_CR10","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1109\/TIP.2017.2651366","volume":"26","author":"Z Li","year":"2017","unstructured":"Li Z, Wei Z, Wen C, Zheng J (2017) Detail-enhanced multi-scale exposure fusion. IEEE Trans Image Process 26(3):1243\u20131252","journal-title":"IEEE Trans Image Process"},{"issue":"4","key":"1762_CR11","doi-asserted-by":"publisher","first-page":"1425","DOI":"10.1109\/TCSVT.2020.3009235","volume":"31","author":"C Zheng","year":"2021","unstructured":"Zheng C, Li Z, Yang Y, Wu S (2021) Single image brightening via multi-scale exposure fusion with hybrid learning. IEEE Trans Circuits Syst Video Technol 31(4):1425\u20131435","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"9","key":"1762_CR12","doi-asserted-by":"publisher","first-page":"4364","DOI":"10.1109\/TIP.2019.2910412","volume":"28","author":"W Ren","year":"2019","unstructured":"Ren W, Liu S, Ma L, Xu Q, Xu X, Cao X, Du J, Yang M-H (2019) Low-light image enhancement via a deep hybrid network. IEEE Trans Image Process 28(9):4364\u20134375","journal-title":"IEEE Trans Image Process"},{"key":"1762_CR13","doi-asserted-by":"crossref","unstructured":"Kim G, Kwon D, Kwon J (2019), Low-lightgan: low-light enhancement via advanced generative adversarial network with task-driven training. In: 2019 IEEE International conference on image processing (ICIP). IEEE, 2811\u20132815","DOI":"10.1109\/ICIP.2019.8803328"},{"key":"1762_CR14","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.patcog.2019.03.019","volume":"92","author":"S Xie","year":"2019","unstructured":"Xie S, Hu H, Wu Y (2019) Deep multi-path convolutional neural network joint with salient region attention for facial expression recognition. Pattern Recogn 92:177\u2013191","journal-title":"Pattern Recogn"},{"key":"1762_CR15","doi-asserted-by":"publisher","first-page":"2016","DOI":"10.1109\/TIP.2021.3049955","volume":"30","author":"H Li","year":"2021","unstructured":"Li H, Wang N, Ding X, Yang X, Gao X (2021) Adaptively learning facial expression representation via cf labels and distillation. IEEE Trans Image Process 30:2016\u20132028","journal-title":"IEEE Trans Image Process"},{"key":"1762_CR16","doi-asserted-by":"crossref","unstructured":"Xue F, Wang Q, Guo G (2021), Transfer: learning relation-aware facial expression representations with transformers. In: Proceedings of the IEEE\/CVF International conference on computer vision, 3601\u20133610","DOI":"10.1109\/ICCV48922.2021.00358"},{"issue":"3","key":"1762_CR17","doi-asserted-by":"publisher","first-page":"1681","DOI":"10.1109\/TCSVT.2021.3056098","volume":"32","author":"X Zhang","year":"2021","unstructured":"Zhang X, Zhang F, Xu C (2021) Joint expression synthesis and representation learning for facial expression recognition. IEEE Trans Circuits Syst Video Technol 32(3):1681\u20131695","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"1762_CR18","doi-asserted-by":"crossref","unstructured":"Haris M, Shakhnarovich G, Ukita N (2021) Task-driven super resolution: Object detection in low-resolution images. In: International conference on neural information processing. Springer, New York, 387\u2013395","DOI":"10.1007\/978-3-030-92307-5_45"},{"key":"1762_CR19","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.inffus.2021.12.004","volume":"82","author":"L Tang","year":"2022","unstructured":"Tang L, Yuan J, Ma J (2022) Image fusion in the loop of high-level vision tasks: a semantic-aware real-time infrared and visible image fusion network. Inf Fusion 82:28\u201342","journal-title":"Inf Fusion"},{"key":"1762_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108629","volume":"127","author":"T Yan","year":"2022","unstructured":"Yan T, Shi J, Li H, Luo Z, Wang Z (2022) Discriminative information restoration and extraction for weakly supervised low-resolution fine-grained image recognition. Pattern Recogn 127:108629","journal-title":"Pattern Recogn"},{"key":"1762_CR21","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1762_CR22","doi-asserted-by":"crossref","unstructured":"Zhao H, Kong X, He J, Qiao Y, Dong C (2020) Efficient image super-resolution using pixel attention. In: European conference on computer vision. Springer, New York 56\u201372","DOI":"10.1007\/978-3-030-67070-2_3"},{"key":"1762_CR23","doi-asserted-by":"crossref","unstructured":"Huang H, He R, Sun Z, Tan T (2017) Wavelet-srnet: a wavelet-based cnn for multi-scale face super resolution. In: Proceedings of the IEEE international conference on computer vision, 1689\u20131697","DOI":"10.1109\/ICCV.2017.187"},{"key":"1762_CR24","doi-asserted-by":"crossref","unstructured":"Qian R, Tan R.\u00a0T, Yang W, Su J, Liu J (2018) Attentive generative adversarial network for raindrop removal from a single image. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2482\u20132491","DOI":"10.1109\/CVPR.2018.00263"},{"key":"1762_CR25","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"issue":"4","key":"1762_CR26","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612","journal-title":"IEEE Trans Image Process"},{"issue":"7","key":"1762_CR27","doi-asserted-by":"publisher","first-page":"2175","DOI":"10.1007\/s11263-021-01466-8","volume":"129","author":"F Lv","year":"2021","unstructured":"Lv F, Li Y, Lu F (2021) Attention guided low-light image enhancement with a large scale low-light simulation dataset. Int J Comput Vis 129(7):2175\u20132193","journal-title":"Int J Comput Vis"},{"key":"1762_CR28","unstructured":"Wei C, Wang W, Yang W, Liu J (2018) Deep retinex decomposition for low-light enhancement. arXiv preprint arXiv:1808.04560"},{"issue":"9","key":"1762_CR29","doi-asserted-by":"publisher","first-page":"3538","DOI":"10.1109\/TIP.2013.2261309","volume":"22","author":"S Wang","year":"2013","unstructured":"Wang S, Zheng J, Hu H-M, Li B (2013) Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Trans Image Process 22(9):3538\u20133548","journal-title":"IEEE Trans Image Process"},{"key":"1762_CR30","doi-asserted-by":"crossref","unstructured":"Li S, Deng W, Du J (2017) Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2852\u20132861","DOI":"10.1109\/CVPR.2017.277"},{"key":"1762_CR31","doi-asserted-by":"crossref","unstructured":"Barsoum E, Zhang C, Ferrer CC, Zhang Z (2016) Training deep networks for facial expression recognition with crowd-sourced label distribution. In: Proceedings of the 18th ACM International conference on multimodal interaction, , 279\u2013283","DOI":"10.1145\/2993148.2993165"},{"issue":"4","key":"1762_CR32","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612","journal-title":"IEEE Trans Image Process"},{"key":"1762_CR33","doi-asserted-by":"crossref","unstructured":"Selvaraju R.\u00a0R, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International conference on computer vision (ICCV)","DOI":"10.1109\/ICCV.2017.74"},{"issue":"2","key":"1762_CR34","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1109\/TIP.2016.2639450","volume":"26","author":"X Guo","year":"2016","unstructured":"Guo X, Li Y, Ling H (2016) Lime: low-light image enhancement via illumination map estimation. IEEE Trans Image Process 26(2):982\u2013993","journal-title":"IEEE Trans Image Process"},{"key":"1762_CR35","doi-asserted-by":"publisher","first-page":"2340","DOI":"10.1109\/TIP.2021.3051462","volume":"30","author":"Y Jiang","year":"2021","unstructured":"Jiang Y, Gong X, Liu D, Cheng Y, Fang C, Shen X, Yang J, Zhou P, Wang Z (2021) Enlightengan: deep light enhancement without paired supervision. IEEE Trans Image Process 30:2340\u20132349","journal-title":"IEEE Trans Image Process"},{"key":"1762_CR36","doi-asserted-by":"publisher","first-page":"7984","DOI":"10.1109\/TIP.2020.3008396","volume":"29","author":"L-W Wang","year":"2020","unstructured":"Wang L-W, Liu Z-S, Siu W-C, Lun DP (2020) Lightening network for low-light image enhancement. IEEE Trans Image Process 29:7984\u20137996","journal-title":"IEEE Trans Image Process"},{"issue":"4","key":"1762_CR37","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1007\/s11263-020-01407-x","volume":"129","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Guo X, Ma J, Liu W, Zhang J (2021) Beyond brightening low-light images. Int J Comput Vis 129(4):1013\u20131037","journal-title":"Int J Comput Vis"},{"key":"1762_CR38","doi-asserted-by":"crossref","unstructured":"Zhang Y, Zhang J, Guo X (2019), Kindling the darkness: a practical low-light image enhancer, in: Proceedings of the 27th ACM international conference on multimedia, ,1632\u20131640","DOI":"10.1145\/3343031.3350926"},{"key":"1762_CR39","doi-asserted-by":"crossref","unstructured":"Guo C, Li C, Guo J, Loy C.\u00a0C, Hou J, Kwong S, Cong R (2020) Zero-reference deep curve estimation for low-light image enhancement. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 1780\u20131789","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"1762_CR40","unstructured":"Ying Z, Li G, Gao W (2017) A bio-inspired multi-exposure fusion framework for low-light image enhancement. arXiv preprint arXiv:1711.00591"},{"key":"1762_CR41","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.patrec.2018.01.010","volume":"104","author":"C Li","year":"2018","unstructured":"Li C, Guo J, Porikli F, Pang Y (2018) Lightennet: a convolutional neural network for weakly illuminated image enhancement. Pattern Recogn Lett 104:15\u201322","journal-title":"Pattern Recogn Lett"},{"key":"1762_CR42","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1016\/j.patcog.2016.06.008","volume":"61","author":"KG Lore","year":"2017","unstructured":"Lore KG, Akintayo A, Sarkar S (2017) Llnet: a deep autoencoder approach to natural low-light image enhancement. Pattern Recogn 61:650\u2013662","journal-title":"Pattern Recogn"},{"key":"1762_CR43","unstructured":"Wei C, Wang W, Yang W, Liu J (2018) Deep retinex decomposition for low-light enhancement. arXiv preprint arXiv:1808.04560"},{"key":"1762_CR44","doi-asserted-by":"crossref","unstructured":"Xu X, Wang R, Fu C-W, Jia J (2022) Snr-aware low-light image enhancement. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 17714\u201317724","DOI":"10.1109\/CVPR52688.2022.01719"},{"key":"1762_CR45","doi-asserted-by":"crossref","unstructured":"Wang Z, Cun X, Bao J, Zhou W, Liu J, Li H (2022) Uformer: a general u-shaped transformer for image restoration. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 17683\u201317693","DOI":"10.1109\/CVPR52688.2022.01716"},{"key":"1762_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109039","volume":"133","author":"X Liu","year":"2023","unstructured":"Liu X, Ma W, Ma X, Wang J (2023) Lae-net: a locally-adaptive embedding network for low-light image enhancement. Pattern Recogn 133:109039","journal-title":"Pattern Recogn"},{"key":"1762_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118920","volume":"213","author":"X Yang","year":"2023","unstructured":"Yang X, Gong J, Wu L, Yang Z, Shi Y, Nie F (2023) Reference-free low-light image enhancement by associating hierarchical wavelet representations. Expert Syst Appl 213:118920","journal-title":"Expert Syst Appl"},{"key":"1762_CR48","doi-asserted-by":"crossref","unstructured":"Ying Z, Li G, Ren Y, Wang R, Wang W (2017) A new low-light image enhancement algorithm using camera response model. In: Proceedings of the IEEE international conference on computer vision workshops, 3015\u20133022","DOI":"10.1109\/ICCVW.2017.356"},{"key":"1762_CR49","doi-asserted-by":"crossref","unstructured":"Wang K, Peng X, Yang J, Lu S, Qiao Y (2020) Suppressing uncertainties for large-scale facial expression recognition. IEEE\/CVF Conf Comput Vis Pattern Recognit (CVPR) 2020:6896\u20136905","DOI":"10.1109\/CVPR42600.2020.00693"},{"key":"1762_CR50","doi-asserted-by":"crossref","unstructured":"Farzaneh A.\u00a0H, Qi X (2021) Facial expression recognition in the wild via deep attentive center loss. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, 2402\u20132411","DOI":"10.1109\/WACV48630.2021.00245"},{"key":"1762_CR51","doi-asserted-by":"crossref","unstructured":"Li H, Wang N, Ding X, Yang X, Gao X (2021) Adaptively learning facial expression representation via cf labels and distillation. IEEE Trans Image Process 30:2016\u20132028","DOI":"10.1109\/TIP.2021.3049955"},{"key":"1762_CR52","first-page":"17616","volume":"34","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Wang C, Deng W (2021) Relative uncertainty learning for facial expression recognition. Adv Neural Inf Process Syst 34:17616\u201317627","journal-title":"Adv Neural Inf Process Syst"},{"key":"1762_CR53","doi-asserted-by":"crossref","unstructured":"Zhang Y, Wang C, Ling X, Deng W (2022) Learn from all: Erasing attention consistency for noisy label facial expression recognition. In: European conference on computer vision. Springer, New York, 418\u2013434","DOI":"10.1007\/978-3-031-19809-0_24"},{"key":"1762_CR54","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1016\/j.ins.2022.11.068","volume":"619","author":"C Liu","year":"2023","unstructured":"Liu C, Hirota K, Dai Y (2023) Patch attention convolutional vision transformer for facial expression recognition with occlusion. Inf Sci 619:781\u2013794","journal-title":"Inf Sci"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01762-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01762-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01762-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T16:33:14Z","timestamp":1738945994000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01762-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,9]]},"references-count":54,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["1762"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01762-z","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,9]]},"assertion":[{"value":"20 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There is no competition for interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"139"}}