{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T13:39:23Z","timestamp":1780580363439,"version":"3.54.1"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Zhejiang Province Natural Science Foundation","doi-asserted-by":"publisher","award":["LZ25F020010"],"award-info":[{"award-number":["LZ25F020010"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Zhejiang Province Natural Science Foundation","doi-asserted-by":"publisher","award":["ZCLMS25F0201"],"award-info":[{"award-number":["ZCLMS25F0201"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003467","name":"Hangzhou Dianzi University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003467","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62402151"],"award-info":[{"award-number":["62402151"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202131"],"award-info":[{"award-number":["62202131"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition Letters"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1016\/j.patrec.2026.01.007","type":"journal-article","created":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T17:06:25Z","timestamp":1768323985000},"page":"87-94","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["MM-Net: Facial expression recognition based on multi-level and multi-scale attention mechanisms"],"prefix":"10.1016","volume":"201","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-2152-0446","authenticated-orcid":false,"given":"Dongjing","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0004-5736-3029","authenticated-orcid":false,"given":"Hao","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-3416-839X","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0008-0951-4178","authenticated-orcid":false,"given":"Na","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0008-5864-9431","authenticated-orcid":false,"given":"Wenxiu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0009-8543-8637","authenticated-orcid":false,"given":"Jinlin","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-5015-6095","authenticated-orcid":false,"given":"Shuiguang","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.patrec.2026.01.007_bib0001","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.patrec.2022.01.013","article-title":"CERN: Compact facial expression recognition net","volume":"155","author":"Gera","year":"2022","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.patrec.2026.01.007_bib0002","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.patrec.2023.09.015","article-title":"The Florence multi-resolution 3D facial expression dataset","volume":"175","author":"Ferrari","year":"2023","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.patrec.2026.01.007_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.111762","article-title":"A gradual self distillation network with adaptive channel attention for facial expression recognition","volume":"161","author":"Zhang","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.patrec.2026.01.007_bib0004","doi-asserted-by":"crossref","first-page":"4057","DOI":"10.1109\/TIP.2019.2956143","article-title":"Region attention networks for pose and occlusion robust facial expression recognition","volume":"29","author":"Wang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patrec.2026.01.007_bib0005","doi-asserted-by":"crossref","first-page":"6544","DOI":"10.1109\/TIP.2021.3093397","article-title":"Learning deep global multi-scale and local attention features for facial expression recognition in the wild","volume":"30","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patrec.2026.01.007_bib0006","unstructured":"H. Li, M. Sui, F. Zhao, Z. Zha, F. Wu, MVT: mask vision transformer for facial expression recognition in the wild, arXiv: 2106.04520. (2021)."},{"key":"10.1016\/j.patrec.2026.01.007_bib0007","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"6248","article-title":"Dive into ambiguity: Latent distribution mining and pairwise uncertainty estimation for facial expression recognition","author":"She","year":"2021"},{"key":"10.1016\/j.patrec.2026.01.007_bib0008","series-title":"European Conference on Computer Vision","first-page":"818","article-title":"Visualizing and understanding convolutional networks","author":"Zeiler","year":"2014"},{"key":"10.1016\/j.patrec.2026.01.007_bib0009","series-title":"2004 International Conference on Image Processing, 2004. ICIP\u201904.","first-page":"1269","article-title":"A new facial expression recognition technique using 2D DCT and k-means algorithm","volume":"2","author":"Ma","year":"2004"},{"issue":"6","key":"10.1016\/j.patrec.2026.01.007_bib0010","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1016\/j.imavis.2008.08.005","article-title":"Facial expression recognition based on local binary patterns: a comprehensive study","volume":"27","author":"Shan","year":"2009","journal-title":"Image Vis. Comput."},{"key":"10.1016\/j.patrec.2026.01.007_bib0011","series-title":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","first-page":"5","article-title":"Haar features for FACS AU recognition","author":"Whitehill","year":"2006"},{"key":"10.1016\/j.patrec.2026.01.007_bib0012","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patrec.2024.03.007","article-title":"Multimodal prediction of student performance: a fusion of signed graph neural networks and large language models","volume":"181","author":"Wang","year":"2024","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.patrec.2026.01.007_bib0013","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patrec.2024.11.005","article-title":"FM-detector: end-to-end flight maneuver recognition method based on flight data","volume":"187","author":"Wang","year":"2025","journal-title":"Pattern Recognit. Lett."},{"issue":"3","key":"10.1016\/j.patrec.2026.01.007_bib0014","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1140\/epjs\/s11734-024-01162-x","article-title":"Noise-induced alternations and data-driven parameter estimation of a stochastic perceptual model","volume":"234","author":"Wang","year":"2025","journal-title":"Eur. Phys. J. Spec. Top."},{"issue":"5","key":"10.1016\/j.patrec.2026.01.007_bib0015","doi-asserted-by":"crossref","first-page":"4211","DOI":"10.1007\/s11071-024-10152-6","article-title":"Fusing deep learning features for parameter identification of a stochastic airfoil system","volume":"113","author":"Feng","year":"2025","journal-title":"Nonlinear Dyn."},{"key":"10.1016\/j.patrec.2026.01.007_bib0016","series-title":"International Conference on Artificial Neural Networks","first-page":"84","article-title":"Multi-region ensemble convolutional neural network for facial expression recognition","author":"Fan","year":"2018"},{"key":"10.1016\/j.patrec.2026.01.007_bib0017","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"7660","article-title":"Feature decomposition and reconstruction learning for effective facial expression recognition","author":"Ruan","year":"2021"},{"issue":"5","key":"10.1016\/j.patrec.2026.01.007_bib0018","doi-asserted-by":"crossref","first-page":"3190","DOI":"10.1109\/TCSVT.2021.3103782","article-title":"Self-supervised exclusive-inclusive interactive learning for multi-label facial expression recognition in the wild","volume":"32","author":"Li","year":"2021","journal-title":"IEEE Trans. Circuit. Syst. Video Technol."},{"issue":"8","key":"10.1016\/j.patrec.2026.01.007_bib0019","first-page":"1","article-title":"Scaled background swap: video augmentation for action quality assessment with background debiasing","volume":"21","author":"Zhang","year":"2025","journal-title":"ACM Transact. Multimed. Comput. Commun. Applic."},{"key":"10.1016\/j.patrec.2026.01.007_bib0020","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patrec.2024.02.016","article-title":"Attention based multi-task interpretable graph convolutional network for Alzheimer\u2019s disease analysis","volume":"180","author":"Jiang","year":"2024","journal-title":"Pattern Recognit. Lett."},{"issue":"5","key":"10.1016\/j.patrec.2026.01.007_bib0021","doi-asserted-by":"crossref","first-page":"2439","DOI":"10.1109\/TIP.2018.2886767","article-title":"Occlusion aware facial expression recognition using CNN with attention mechanism","volume":"28","author":"Li","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patrec.2026.01.007_bib0022","unstructured":"J.-H. Kim, N. Kim, C.S. Won, Facial expression recognition with swin transformer, arXiv: 2203.13472. (2022)."},{"key":"10.1016\/j.patrec.2026.01.007_bib0023","article-title":"Facial expression recognition with deeply-supervised attention network","author":"Fan","year":"2020","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.patrec.2026.01.007_bib0024","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.patrec.2026.01.007_bib0025","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"key":"10.1016\/j.patrec.2026.01.007_bib0026","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2852","article-title":"Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild","author":"Li","year":"2017"},{"key":"10.1016\/j.patrec.2026.01.007_bib0027","series-title":"Proceedings of the 18th ACM International Conference on Multimodal Interaction","first-page":"279","article-title":"Training deep networks for facial expression recognition with crowd-sourced label distribution","author":"Barsoum","year":"2016"},{"issue":"1","key":"10.1016\/j.patrec.2026.01.007_bib0028","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/TAFFC.2017.2740923","article-title":"Affectnet: a database for facial expression, valence, and arousal computing in the wild","volume":"10","author":"Mollahosseini","year":"2017","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.patrec.2026.01.007_bib0029","series-title":"2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)","first-page":"2106","article-title":"Static facial expression analysis in tough conditions: data, evaluation protocol and benchmark","author":"Dhall","year":"2011"},{"key":"10.1016\/j.patrec.2026.01.007_bib0030","series-title":"Proceedings of the European Conference on Computer Vision (ECCV)","first-page":"222","article-title":"Facial expression recognition with inconsistently annotated datasets","author":"Zeng","year":"2018"},{"issue":"3\u20134","key":"10.1016\/j.patrec.2026.01.007_bib0031","article-title":"FaceCaps for facial expression recognition","volume":"32","author":"Wu","year":"2021","journal-title":"Comput. Animat. Virt. World."},{"key":"10.1016\/j.patrec.2026.01.007_bib0032","doi-asserted-by":"crossref","first-page":"2016","DOI":"10.1109\/TIP.2021.3049955","article-title":"Adaptively learning facial expression representation via cf labels and distillation","volume":"30","author":"Li","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patrec.2026.01.007_bib0033","first-page":"17616","article-title":"Relative uncertainty learning for facial expression recognition","volume":"34","author":"Zhang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patrec.2026.01.007_bib0034","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"13984","article-title":"Label distribution learning on auxiliary label space graphs for facial expression recognition","author":"Chen","year":"2020"},{"key":"10.1016\/j.patrec.2026.01.007_bib0035","series-title":"European Conference on Computer Vision","first-page":"418","article-title":"Learn from all: erasing attention consistency for noisy label facial expression recognition","author":"Zhang","year":"2022"},{"key":"10.1016\/j.patrec.2026.01.007_bib0036","doi-asserted-by":"crossref","DOI":"10.1109\/TII.2022.3233650","article-title":"Efficient facial expression recognition with representation reinforcement network and transfer self-training for human\u2013machine interaction","author":"Jiang","year":"2023","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.patrec.2026.01.007_bib0037","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"3601","article-title":"Transfer: learning relation-aware facial expression representations with transformers","author":"Xue","year":"2021"},{"key":"10.1016\/j.patrec.2026.01.007_bib0038","series-title":"2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)","first-page":"302","article-title":"Island loss for learning discriminative features in facial expression recognition","author":"Cai","year":"2018"},{"issue":"11","key":"10.1016\/j.patrec.2026.01.007_bib0039","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Pattern Recognition Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0167865526000176?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0167865526000176?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T01:44:19Z","timestamp":1773971059000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S0167865526000176"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":39,"alternative-id":["S0167865526000176"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.patrec.2026.01.007","relation":{},"ISSN":["0167-8655"],"issn-type":[{"value":"0167-8655","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MM-Net: Facial expression recognition based on multi-level and multi-scale attention mechanisms","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition Letters","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.patrec.2026.01.007","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}