{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T09:44:07Z","timestamp":1765619047130,"version":"3.48.0"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:00:00Z","timestamp":1758672000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:00:00Z","timestamp":1758672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s13042-025-02802-x","type":"journal-article","created":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T04:12:35Z","timestamp":1758687155000},"page":"10835-10850","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CPL: consistent prompt learning for noisy label facial expression recognition"],"prefix":"10.1007","volume":"16","author":[{"given":"Jinglin","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiangchang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaohui","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinxin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yilong","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huimin","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,24]]},"reference":[{"issue":"3","key":"2802_CR1","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1037\/h0024648","volume":"31","author":"A Mehrabian","year":"1967","unstructured":"Mehrabian A, Ferris SR (1967) Inference of attitudes from nonverbal communication in two channels. J Consult Psychol 31(3):248","journal-title":"J Consult Psychol"},{"key":"2802_CR2","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"},{"key":"2802_CR3","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, pp 3601\u20133610","DOI":"10.1109\/ICCV48922.2021.00358"},{"issue":"4","key":"2802_CR4","doi-asserted-by":"publisher","first-page":"3244","DOI":"10.1109\/TAFFC.2022.3226473","volume":"14","author":"F Xue","year":"2022","unstructured":"Xue F, Wang Q, Tan Z, Ma Z, Guo G (2022) Vision transformer with attentive pooling for robust facial expression recognition. IEEE Trans Affect Comput 14(4):3244\u20133256","journal-title":"IEEE Trans Affect Comput"},{"key":"2802_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107678","volume":"236","author":"F Nan","year":"2022","unstructured":"Nan F, Jing W, Tian F, Zhang J, Chao K-M, Hong Z, Zheng Q (2022) Feature super-resolution based facial expression recognition for multi-scale low-resolution images. Knowl-Based Syst 236:107678","journal-title":"Knowl-Based Syst"},{"key":"2802_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108136","volume":"240","author":"T Ma","year":"2022","unstructured":"Ma T, Tian W, Xie Y (2022) Multi-level knowledge distillation for low-resolution object detection and facial expression recognition. Knowl-Based Syst 240:108136","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"2802_CR7","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s13042-022-01681-w","volume":"15","author":"S Liu","year":"2024","unstructured":"Liu S, Huang S, Fu W, Lin JC-W (2024) A descriptive human visual cognitive strategy using graph neural network for facial expression recognition. Int J Mach Learn Cybern 15(1):19\u201335","journal-title":"Int J Mach Learn Cybern"},{"key":"2802_CR8","doi-asserted-by":"crossref","unstructured":"Zheng J, Li B, Zhang S, Wu S, Cao L, Ding S (2023) Attack can benefit: An adversarial approach to recognizing facial expressions under noisy annotations. In: Proceedings of the AAAI conference on artificial intelligence, pp 3660\u20133668","DOI":"10.1609\/aaai.v37i3.25477"},{"key":"2802_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110451","volume":"269","author":"AR Shahid","year":"2023","unstructured":"Shahid AR, Yan H (2023) Squeezexpnet: dual-stage convolutional neural network for accurate facial expression recognition with attention mechanism. Knowl-Based Syst 269:110451","journal-title":"Knowl-Based Syst"},{"issue":"6","key":"2802_CR10","doi-asserted-by":"publisher","first-page":"2063","DOI":"10.1007\/s13042-023-02016-z","volume":"15","author":"X Jin","year":"2024","unstructured":"Jin X, Song X, Wu X, Yan W (2024) Transformer embedded spectral-based graph network for facial expression recognition. Int J Mach Learn Cybern 15(6):2063\u20132077","journal-title":"Int J Mach Learn Cybern"},{"key":"2802_CR11","doi-asserted-by":"crossref","unstructured":"Tang H, Li Y, Jin Z (2024) A dual stream attention network for facial expression recognition in the wild. Int J Mach Learn Cybern, pp 1\u201318","DOI":"10.1007\/s13042-024-02287-0"},{"key":"2802_CR12","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yao Y, Liu X, Qin L, Wang W, Deng W (2024) Open-set facial expression recognition. In: Wooldridge MJ, Dy JG, Natarajan S (eds) Thirty-eighth AAAI conference on artificial intelligence, AAAI 2024, thirty-sixth conference on innovative applications of artificial intelligence, IAAI 2024, fourteenth symposium on educational advances in artificial intelligence, EAAI 2014, February 20\u201327, 2024, Vancouver, Canada, pp. 646\u2013654","DOI":"10.1609\/aaai.v38i1.27821"},{"key":"2802_CR13","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TIP.2024.3378459","volume":"33","author":"W Xie","year":"2024","unstructured":"Xie W, Peng Z, Shen L, Lu W, Zhang Y, Song S (2024) Cross-layer contrastive learning of latent semantics for facial expression recognition. IEEE Trans Image Process 33:2514\u20132529","journal-title":"IEEE Trans Image Process"},{"issue":"14","key":"2802_CR14","doi-asserted-by":"publisher","first-page":"3814","DOI":"10.1049\/ipr2.12596","volume":"16","author":"V Esmaeili","year":"2022","unstructured":"Esmaeili V, Feghhi MM, Shahdi SO (2022) Spotting micro-movements in image sequence by introducing intelligent cubic-LBP. IET Image Process 16(14):3814\u20133830","journal-title":"IET Image Process"},{"key":"2802_CR15","doi-asserted-by":"publisher","first-page":"151261","DOI":"10.1109\/ACCESS.2024.3479718","volume":"12","author":"MM Feghhi","year":"2024","unstructured":"Feghhi MM, Esmaeili V (2024) Real-time client authentication in cyberspace using the GLBPD and three-stream 3D-CNNs with the LSTM. IEEE Access 12:151261\u2013151274","journal-title":"IEEE Access"},{"issue":"27\u201328","key":"2802_CR16","doi-asserted-by":"publisher","first-page":"20221","DOI":"10.1007\/s11042-020-08737-5","volume":"79","author":"V Esmaeili","year":"2020","unstructured":"Esmaeili V, Shahdi SO (2020) Automatic micro-expression apex spotting using cubic-LBP. Multim Tools Appl 79(27\u201328):20221\u201320239","journal-title":"Multim Tools Appl"},{"issue":"28","key":"2802_CR17","doi-asserted-by":"publisher","first-page":"40089","DOI":"10.1007\/s11042-022-13133-2","volume":"81","author":"V Esmaeili","year":"2022","unstructured":"Esmaeili V, Feghhi MM, Shahdi SO (2022) A comprehensive survey on facial micro-expression: approaches and databases. Multim Tools Appl 81(28):40089\u201340134","journal-title":"Multim Tools Appl"},{"key":"2802_CR18","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, pp 2852\u20132861","DOI":"10.1109\/CVPR.2017.277"},{"key":"2802_CR19","doi-asserted-by":"crossref","unstructured":"Wang K, Peng X, Yang J, Lu S, Qiao Y (2020) Suppressing uncertainties for large-scale facial expression recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6897\u20136906","DOI":"10.1109\/CVPR42600.2020.00693"},{"key":"2802_CR20","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":"2802_CR21","unstructured":"Arpit D, Jastrzebski S, Ballas N, Krueger D, Bengio E, Kanwal MS, Maharaj T, Fischer A, Courville AC, Bengio Y, Lacoste-Julien S (2017) A closer look at memorization in deep networks. In: Proceedings of the 34th international conference on machine learning, ICML 2017, Sydney, NSW, Australia, 6\u201311 August 2017, vol 70, pp 233\u2013242"},{"issue":"3","key":"2802_CR22","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1145\/3446776","volume":"64","author":"C Zhang","year":"2021","unstructured":"Zhang C, Bengio S, Hardt M, Recht B, Vinyals O (2021) Understanding deep learning (still) requires rethinking generalization. Commun ACM 64(3):107\u2013115","journal-title":"Commun ACM"},{"key":"2802_CR23","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, pp 418\u2013434","DOI":"10.1007\/978-3-031-19809-0_24"},{"key":"2802_CR24","doi-asserted-by":"crossref","unstructured":"Wu Z, Cui J (2023) La-net: Landmark-aware learning for reliable facial expression recognition under label noise. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 20698\u201320707","DOI":"10.1109\/ICCV51070.2023.01892"},{"key":"2802_CR25","doi-asserted-by":"crossref","unstructured":"Sari FD, Supeno Ridlo ZR (2023) Pengembangan lkpd berbasis question prompt scaffolding untuk meningkatkan scientific writing skill peserta didik pada pembelajaran ipa smp. Cetta: Jurnal Ilmu Pendidikan 6(3), 500\u2013515","DOI":"10.37329\/cetta.v6i3.2536"},{"issue":"9","key":"2802_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3560815","volume":"55","author":"P Liu","year":"2023","unstructured":"Liu P, Yuan W, Fu J, Jiang Z, Hayashi H, Neubig G (2023) Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. ACM Comput Surv 55(9):1\u201335","journal-title":"ACM Comput Surv"},{"issue":"3","key":"2802_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.metrad.2023.100047","volume":"1","author":"J Wang","year":"2023","unstructured":"Wang J, Liu Z, Zhao L, Wu Z, Ma C, Yu S, Dai H, Yang Q, Liu Y, Zhang S, Shi E, Pan Y, Zhang T, Zhu D, Li X, Jiang X, Ge B, Yuan Y, Shen D, Liu T, Zhang S (2023) Review of large vision models and visual prompt engineering. Meta-Radiol 1(3):100047","journal-title":"Meta-Radiol"},{"key":"2802_CR28","unstructured":"Gu J, Han Z, Chen S, Beirami A, He B, Zhang G, Liao R, Qin Y, Tresp V, Torr P (2023) A systematic survey of prompt engineering on vision-language foundation models. arXiv preprint arXiv:2307.12980"},{"key":"2802_CR29","unstructured":"Li Y, Wang M, Gong M, Lu Y, Liu L (2023) Fer-former: multi-modal transformer for facial expression recognition. arXiv preprint arXiv:2303.12997"},{"key":"2802_CR30","doi-asserted-by":"crossref","unstructured":"Li H, Niu H, Zhu Z, Zhao F (2023) Cliper: A unified vision-language framework for in-the-wild facial expression recognition. arXiv preprint arXiv:2303.00193","DOI":"10.1109\/ICME57554.2024.10687508"},{"key":"2802_CR31","doi-asserted-by":"publisher","first-page":"8038","DOI":"10.1109\/TMM.2024.3374573","volume":"26","author":"Y Lv","year":"2024","unstructured":"Lv Y, Huang G, Yan Y, Xue J, Chen S, Wang H (2024) Visual-textual attribute learning for class-incremental facial expression recognition. IEEE Trans Multim 26:8038\u20138051","journal-title":"IEEE Trans Multim"},{"issue":"2","key":"2802_CR32","doi-asserted-by":"publisher","first-page":"1148","DOI":"10.1109\/TAFFC.2021.3077248","volume":"14","author":"W Hayale","year":"2021","unstructured":"Hayale W, Negi PS, Mahoor M (2021) Deep Siamese neural networks for facial expression recognition in the wild. IEEE Trans Affect Comput 14(2):1148\u20131158","journal-title":"IEEE Trans Affect Comput"},{"key":"2802_CR33","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1007\/s00371-021-02069-7","volume":"38","author":"S Saurav","year":"2022","unstructured":"Saurav S, Gidde P, Saini R, Singh S (2022) Dual integrated convolutional neural network for real-time facial expression recognition in the wild. Vis Comput 38:1083\u20131096","journal-title":"Vis Comput"},{"key":"2802_CR34","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al (2021) An image is worth 16x16 words: Transformers for image recognition at scale. In: International conference on learning representations"},{"issue":"5","key":"2802_CR35","doi-asserted-by":"publisher","first-page":"7216","DOI":"10.1109\/TII.2024.3353912","volume":"20","author":"Y Wang","year":"2024","unstructured":"Wang Y, Yan S, Song W, Liotta A, Liu J, Yang D, Gao S, Zhang W (2024) Mgr$$ ^{\\text{3 }}$$net: multigranularity region relation representation network for facial expression recognition in affective robots. IEEE Trans Ind Inf 20(5):7216\u20137226","journal-title":"IEEE Trans Ind Inf"},{"key":"2802_CR36","doi-asserted-by":"crossref","unstructured":"Li J, Xiong C, Hoi SC (2021) Learning from noisy data with robust representation learning. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 9485\u20139494","DOI":"10.1109\/ICCV48922.2021.00935"},{"issue":"7","key":"2802_CR37","first-page":"3676","volume":"44","author":"M-K Xie","year":"2021","unstructured":"Xie M-K, Huang S-J (2021) Partial multi-label learning with noisy label identification. IEEE Trans Pattern Anal Mach Intell 44(7):3676\u20133687","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2802_CR38","unstructured":"Ma F, Sun B, Li S (2023) Transformer-augmented network with online label correction for facial expression recognition. IEEE Trans Affective Comput, pp 1\u201313"},{"key":"2802_CR39","doi-asserted-by":"publisher","first-page":"6297","DOI":"10.1109\/TMM.2023.3347849","volume":"26","author":"M Li","year":"2024","unstructured":"Li M, Fu H, He S, Fan H, Liu J, Keppo J, Shou MZ (2024) DR-FER: discriminative and robust representation learning for facial expression recognition. IEEE Trans Multim 26:6297\u20136309","journal-title":"IEEE Trans Multim"},{"key":"2802_CR40","doi-asserted-by":"publisher","first-page":"2531","DOI":"10.1109\/TMM.2023.3301209","volume":"26","author":"Y Liu","year":"2024","unstructured":"Liu Y, Zhang X, Kauttonen J, Zhao G (2024) Uncertain facial expression recognition via multi-task assisted correction. IEEE Trans Multim 26:2531\u20132543","journal-title":"IEEE Trans Multim"},{"key":"2802_CR41","doi-asserted-by":"crossref","unstructured":"Jia M, Tang L, Chen BC, Cardie C, Belongie S, Hariharan B, Lim SN (2022) Visual prompt tuning. In: European conference on computer vision, pp 709\u2013727","DOI":"10.1007\/978-3-031-19827-4_41"},{"key":"2802_CR42","unstructured":"Bahng H, Jahanian A, Sankaranarayanan S, Isola P (2022) Exploring visual prompts for adapting large-scale models. arXiv preprint arXiv:2203.17274"},{"key":"2802_CR43","doi-asserted-by":"crossref","unstructured":"Shahid M (2023) Unsupervised dual modality prompt learning for facial expression recognition. In: 2023 8th international conference on computer and communication systems, pp 1056\u20131061","DOI":"10.1109\/ICCCS57501.2023.10151313"},{"issue":"9","key":"2802_CR44","doi-asserted-by":"publisher","first-page":"0162","DOI":"10.1109\/TPAMI.2023.3268118","volume":"45","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Kang B, Hooi B, Yan S, Feng J (2023) Deep long-tailed learning: a survey. IEEE Trans Pattern Anal Mach Intell 45(9):0162\u20138828","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2802_CR45","doi-asserted-by":"crossref","unstructured":"Zhong Y, Deng W, Wang M, Hu J, Peng J, Tao X, Huang Y (2019) Unequal-training for deep face recognition with long-tailed noisy data. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7812\u20137821","DOI":"10.1109\/CVPR.2019.00800"},{"key":"2802_CR46","doi-asserted-by":"crossref","unstructured":"Xu Z, Liu R, Yang S, Chai Z, Yuan C (2023) Learning imbalanced data with vision transformers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 15793\u201315803","DOI":"10.1109\/CVPR52729.2023.01516"},{"key":"2802_CR47","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.neunet.2022.11.025","volume":"158","author":"H Gao","year":"2023","unstructured":"Gao H, Wu M, Chen Z, Li Y, Wang X, An S, Li J, Liu C (2023) SSA-ICL: multi-domain adaptive attention with intra-dataset continual learning for facial expression recognition. Neural Netw 158:228\u2013238","journal-title":"Neural Netw"},{"key":"2802_CR48","unstructured":"Zhang Y, Li Y, Qin L, Liu X, Deng W (2023) Leave no stone unturned: Mine extra knowledge for imbalanced facial expression recognition. In: Oh A, Naumann T, Globerson A, Saenko K, Hardt M, Levine S (eds) Advances in neural information processing systems 36: annual conference on neural information processing systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10\u201316, 2023"},{"key":"2802_CR49","doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2921\u20132929","DOI":"10.1109\/CVPR.2016.319"},{"issue":"6","key":"2802_CR50","doi-asserted-by":"publisher","first-page":"2791","DOI":"10.1109\/TIP.2018.2809040","volume":"27","author":"B-S Oh","year":"2018","unstructured":"Oh B-S, Toh K-A, Teoh ABJ, Lin Z (2018) An analytic gabor feedforward network for single-sample and pose-invariant face recognition. IEEE Trans Image Process 27(6):2791\u20132805","journal-title":"IEEE Trans Image Process"},{"key":"2802_CR51","doi-asserted-by":"crossref","unstructured":"Zhu L, Chen T, Yin J, See S, Liu J (2023) Learning gabor texture features for fine-grained recognition. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 1621\u20131631","DOI":"10.1109\/ICCV51070.2023.00156"},{"key":"2802_CR52","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, pp 279\u2013283","DOI":"10.1145\/2993148.2993165"},{"issue":"1","key":"2802_CR53","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/TAFFC.2017.2740923","volume":"10","author":"A Mollahosseini","year":"2017","unstructured":"Mollahosseini A, Hasani B, Mahoor MH (2017) Affectnet: a database for facial expression, valence, and arousal computing in the wild. IEEE Trans Affect Comput 10(1):18\u201331","journal-title":"IEEE Trans Affect Comput"},{"key":"2802_CR54","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, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2802_CR55","doi-asserted-by":"crossref","unstructured":"Guo Y, Zhang L, Hu Y, He X, Gao J (2016) Ms-celeb-1m: A dataset and benchmark for large-scale face recognition. In European Conference on Computer Vision, pp. 87\u2013102","DOI":"10.1007\/978-3-319-46487-9_6"},{"issue":"10","key":"2802_CR56","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang K, Zhang Z, Li Z, Qiao Y (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett 23(10):1499\u20131503","journal-title":"IEEE Signal Process Lett"},{"key":"2802_CR57","unstructured":"Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: International conference on learning representations"},{"key":"2802_CR58","doi-asserted-by":"crossref","unstructured":"Le N, Nguyen K, Tran Q, Tjiputra E, Le B, Nguyen A (2023) Uncertainty-aware label distribution learning for facial expression recognition. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 6088\u20136097","DOI":"10.1109\/WACV56688.2023.00603"},{"key":"2802_CR59","unstructured":"Azadi S, Feng J, Jegelka S, Darrell T (2016) Auxiliary image regularization for deep cnns with noisy labels. In: International conference on learning representations"},{"key":"2802_CR60","unstructured":"Goldberger J, Ben-Reuven E (2017) Training deep neural-networks using a noise adaptation layer. In: International conference on learning representations"},{"key":"2802_CR61","doi-asserted-by":"crossref","unstructured":"Zeng J, Shan S, Chen X (2018) Facial expression recognition with inconsistently annotated datasets. In: Proceedings of the European conference on computer vision, pp 222\u2013237","DOI":"10.1007\/978-3-030-01261-8_14"},{"key":"2802_CR62","doi-asserted-by":"crossref","unstructured":"Chen S, Wang J, Chen Y, Shi Z, Geng X, Rui Y (2020) Label distribution learning on auxiliary label space graphs for facial expression recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13984\u201313993","DOI":"10.1109\/CVPR42600.2020.01400"},{"key":"2802_CR63","doi-asserted-by":"crossref","unstructured":"Howard A, Sandler M, Chu G, Chen LC, Chen B, Tan M, Wang W, Zhu Y, Pang R, Vasudevan V, et al (2019) Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 1314\u20131324","DOI":"10.1109\/ICCV.2019.00140"},{"key":"2802_CR64","doi-asserted-by":"crossref","unstructured":"Guo H, Zheng K, Fan X, Yu H, Wang S (2019) Visual attention consistency under image transforms for multi-label image classification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 729\u2013739","DOI":"10.1109\/CVPR.2019.00082"},{"key":"2802_CR65","doi-asserted-by":"crossref","unstructured":"Cui Y, Jia M, Lin TY, Song Y, Belongie S (2019) Class-balanced loss based on effective number of samples. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9268\u20139277","DOI":"10.1109\/CVPR.2019.00949"},{"key":"2802_CR66","doi-asserted-by":"crossref","unstructured":"Zhong Z, Cui J, Liu S, Jia J (2021) Improving calibration for long-tailed recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 16489\u201316498","DOI":"10.1109\/CVPR46437.2021.01622"},{"issue":"11","key":"2802_CR67","first-page":"2579","volume":"9","author":"L Maaten","year":"2008","unstructured":"Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(11):2579\u20132605","journal-title":"J Mach Learn Res"},{"key":"2802_CR68","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, 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, pp 618\u2013626","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02802-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02802-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02802-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T09:41:23Z","timestamp":1765618883000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02802-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,24]]},"references-count":68,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["2802"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02802-x","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2025,9,24]]},"assertion":[{"value":"24 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 September 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":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}