{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T14:40:56Z","timestamp":1769265656488,"version":"3.49.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T00:00:00Z","timestamp":1681776000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T00:00:00Z","timestamp":1681776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012456","name":"National Social Science Fund of China","doi-asserted-by":"publisher","award":["22ZDA121"],"award-info":[{"award-number":["22ZDA121"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s12652-023-04602-z","type":"journal-article","created":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T06:02:39Z","timestamp":1681797759000},"page":"7937-7948","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["GREB: gradient re-balanced loss for long-tailed multi-lable classification"],"prefix":"10.1007","volume":"14","author":[{"given":"Zheng","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4143-6399","authenticated-orcid":false,"given":"Kehua","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyuan","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liwei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,18]]},"reference":[{"key":"4602_CR1","first-page":"4034","volume":"1","author":"F Akhbardeh","year":"2021","unstructured":"Akhbardeh F, Alm CO, Zampieri M, Desell T (2021) Handling extreme class imbalance in technical logbook datasets. Proc Annu Meet Assoc Comput Linguist Int Jt Conf Ntl Lang Process 1:4034\u20134045","journal-title":"Proc Annu Meet Assoc Comput Linguist Int Jt Conf Ntl Lang Process"},{"issue":"8","key":"4602_CR2","doi-asserted-by":"publisher","first-page":"4077","DOI":"10.1007\/s12652-021-03323-5","volume":"13","author":"T Alafif","year":"2022","unstructured":"Alafif T, Alzahrani B, Cao Y, Alotaibi R, Barnawi A, Chen M (2022) Generative adversarial network based abnormal behavior detection in massive crowd videos: a hajj case study. J Ambient Intell Humaniz Comput 13(8):4077\u20134088","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"4602_CR3","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","volume":"106","author":"M Buda","year":"2018","unstructured":"Buda M, Maki A, Mazurowski MA (2018) A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw 106:249\u2013259","journal-title":"Neural Netw"},{"key":"4602_CR4","doi-asserted-by":"crossref","unstructured":"Cai J, Wang Y, Hwang J-N (2021) Ace: ally complementary experts for solving long-tailed recognition in one-shot. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 112\u2013121","DOI":"10.1109\/ICCV48922.2021.00018"},{"key":"4602_CR5","first-page":"32","volume-title":"Advances in neural information processing systems","author":"K Cao","year":"2019","unstructured":"Cao K, Wei C, Gaidon A, Arechiga N, Ma T (2019) Learning imbalanced datasets with label-distribution-aware margin loss. Advances in neural information processing systems. Springer, Cham, p 32"},{"key":"4602_CR6","doi-asserted-by":"crossref","unstructured":"Cao D, Zhu X, Huang X, Guo J, Lei Z (2020). Domain balancing: face recognition on long-tailed domains. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 5671\u20135679","DOI":"10.1109\/CVPR42600.2020.00571"},{"key":"4602_CR7","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"key":"4602_CR8","first-page":"1","volume":"1","author":"G Chinnappa","year":"2021","unstructured":"Chinnappa G, Rajagopal MK (2021) Residual attention network for deep face recognition using micro-expression image analysis. J Ambient Intell Humaniz Comput 1:1\u201314","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"4602_CR9","doi-asserted-by":"crossref","unstructured":"Cui Y, Jia M, Lin T-Y, 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":"4602_CR10","first-page":"79","volume":"70","author":"X Dai","year":"2019","unstructured":"Dai X (2019) Hybridnet: a fast vehicle detection system for autonomous driving. Signal Process 70:79\u201388","journal-title":"Signal Process"},{"issue":"9","key":"4602_CR11","doi-asserted-by":"publisher","first-page":"4175","DOI":"10.1007\/s12652-021-03609-8","volume":"13","author":"L\u00f3pez V De Arriba","year":"2022","unstructured":"De Arriba L\u00f3pez V, Cobos-Guzman S (2022) Development of a deep learning model for recognising traffic sings focused on difficult cases. J Ambient Intell Humaniz Comput 13(9):4175\u20134187","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"4602_CR12","doi-asserted-by":"crossref","unstructured":"Deng Z, Liu H, Wang Y, Wang C, Yu Z, Sun X (2021). Pml: Progressive margin loss for long-tailed age classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 10503\u201310512","DOI":"10.1109\/CVPR46437.2021.01036"},{"issue":"1","key":"4602_CR13","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/s12652-020-01998-w","volume":"12","author":"S Devunooru","year":"2021","unstructured":"Devunooru S, Alsadoon A, Chandana P, Beg A (2021) Deep learning neural networks for medical image segmentation of brain tumours for diagnosis: a recent review and taxonomy. J Ambient Intell Humaniz Comput 12(1):455\u2013483","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"4602_CR14","volume-title":"Advances in neural information processing systems","author":"R Ding","year":"2022","unstructured":"Ding R, Guo K, Zhu X, Wu Z, Wang L (2022) ComGAN: unsupervised disentanglement and segmentation via image composition. In: Oh AH, Agarwal A, Belgrave D, Cho K (eds) Advances in neural information processing systems. Springer, Cham"},{"key":"4602_CR15","doi-asserted-by":"crossref","unstructured":"Duarte K, Rawat Y, Shah M (2021) Plm: Partial label masking for imbalanced multi-label classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 2739\u20132748","DOI":"10.1109\/CVPRW53098.2021.00308"},{"key":"4602_CR16","unstructured":"Gidaris S, Singh P, Komodakis N (2018) Unsupervised representation learning by predicting image rotations. arXiv:1803.07728"},{"key":"4602_CR17","doi-asserted-by":"crossref","unstructured":"Gu C, Sun C, Ross D.\u00a0A, Vondrick C, Pantofaru C, Li Y, Vijayanarasimhan S, Toderici G, Ricco S, Sukthankar R, et\u00a0al. (2018) Ava: a video dataset of spatio-temporally localized atomic visual actions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 6047\u20136056","DOI":"10.1109\/CVPR.2018.00633"},{"key":"4602_CR18","first-page":"1","volume":"1","author":"Z Halim","year":"2022","unstructured":"Halim Z, Sulaiman M, Waqas M, Ayd\u0131n D (2022) Deep neural network-based identification of driving risk utilizing driver dependent vehicle driving features: a scheme for critical infrastructure protection. J Ambient Intell Humaniz Comput 1:1\u201319","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"4602_CR19","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\/CVF Conference on Computer Vision and Pattern Recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"4602_CR20","first-page":"15","volume":"3","author":"T-I Hsieh","year":"2021","unstructured":"Hsieh T-I, Robb E, Chen H-T, Huang J-B (2021) Droploss for long-tail instance segmentation. AAAI 3:15","journal-title":"AAAI"},{"key":"4602_CR21","doi-asserted-by":"crossref","unstructured":"Huang C, Li Y, Loy CC, Tang X (2016) Learning deep representation for imbalanced classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 5375\u20135384","DOI":"10.1109\/CVPR.2016.580"},{"key":"4602_CR22","doi-asserted-by":"crossref","unstructured":"Jamal M.\u00a0A, Brown M, Yang M-H, Wang L, Gong B (2020). Rethinking class-balanced methods for long-tailed visual recognition from a domain adaptation perspective. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 7610\u20137619","DOI":"10.1109\/CVPR42600.2020.00763"},{"key":"4602_CR23","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1109\/TPAMI.2019.2929166","volume":"43","author":"X-Y Jing","year":"2021","unstructured":"Jing X-Y, Zhang X, Zhu X, Wu F, You X, Gao Y, Shan S, Yang J-Y (2021) Multiset feature learning for highly imbalanced data classification. IEEE Trans Pattern Anal Mach Intell 43:139\u2013156","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4602_CR24","unstructured":"Kang B, Xie S, Rohrbach M, Yan Z, Gordo A, Feng J, Kalantidis Y (2020) Decoupling representation and classifier for long-tailed recognition"},{"key":"4602_CR25","doi-asserted-by":"crossref","unstructured":"Korycki \u0141, Krawczyk B (2021). Concept drift detection from multi-class imbalanced data streams. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), IEEE, pp 1068\u20131079","DOI":"10.1109\/ICDE51399.2021.00097"},{"key":"4602_CR26","unstructured":"Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images. Technical Report"},{"key":"4602_CR27","first-page":"1","volume":"1","author":"Y Kumar","year":"2022","unstructured":"Kumar Y, Koul A, Singla R, Ijaz MF (2022) Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Humaniz Comput 1:1\u201328","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"4602_CR44","doi-asserted-by":"crossref","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. In: Proceedings of the IEEE, pp 2278\u20132324","DOI":"10.1109\/5.726791"},{"key":"4602_CR28","doi-asserted-by":"crossref","unstructured":"Li Y Wang T, Kang B, Tang S, Wang C, Li J, Feng J (2020a) Overcoming classifier imbalance for long-tail object detection with balanced group softmax. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 10991\u201311000","DOI":"10.1109\/CVPR42600.2020.01100"},{"key":"4602_CR29","doi-asserted-by":"crossref","unstructured":"Li T, Cao P, Yuan Y, Fan L, Yang Y, Feris R, Indyk P, Katabi D (2021b) Targeted supervised contrastive learning for long-tailed recognition. arXiv:2111.13998","DOI":"10.1109\/CVPR52688.2022.00679"},{"key":"4602_CR30","doi-asserted-by":"crossref","unstructured":"Li S, Gong K, Liu C\u00a0H, Wang Y, Qiao F, Cheng X (2021c). Metasaug: Meta semantic augmentation for long-tailed visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 5212\u20135221","DOI":"10.1109\/CVPR46437.2021.00517"},{"key":"4602_CR31","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer vision - ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: common objects in context. Computer vision - ECCV 2014. Springer, Cham, pp 740\u2013755"},{"key":"4602_CR32","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2017). Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"4602_CR33","doi-asserted-by":"crossref","unstructured":"Liu Z, Miao Z, Zhan X, Wang J, Gong B, Yu S.\u00a0X (2019). Large-scale long-tailed recognition in an open world. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 2537\u20132546","DOI":"10.1109\/CVPR.2019.00264"},{"key":"4602_CR34","doi-asserted-by":"crossref","unstructured":"Ren J, Zhang M, Yu C, Liu Z (2022) Balanced mse for imbalanced visual regression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 7926\u20137935","DOI":"10.1109\/CVPR52688.2022.00777"},{"key":"4602_CR36","doi-asserted-by":"crossref","unstructured":"Tan J, Wang C, Li B, Li Q, Ouyang W, Yin C,Yan J (2020). Equalization loss for long-tailed object recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 11662\u201311671","DOI":"10.1109\/CVPR42600.2020.01168"},{"key":"4602_CR35","doi-asserted-by":"crossref","unstructured":"Tan J, Lu X, Zhang G, Yin C, Li Q (2021). Equalization loss v2: A new gradient balance approach for long-tailed object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 1685\u20131694","DOI":"10.1109\/CVPR46437.2021.00173"},{"key":"4602_CR37","doi-asserted-by":"crossref","unstructured":"Tian J, Chen S, Zhang X, Feng Z, Xiong D, Wu S, Dou C (2021) Re-embedding difficult samples via mutual information constrained semantically oversampling for imbalanced text classification. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp 3148\u20133161","DOI":"10.18653\/v1\/2021.emnlp-main.252"},{"key":"4602_CR38","first-page":"30","volume-title":"Advances in neural information processing systems","author":"Y-X Wang","year":"2017","unstructured":"Wang Y-X, Ramanan D, Hebert M (2017) Learning to model the tail. In: Ch M (ed) Advances in neural information processing systems. Springer, Cham, p 30"},{"key":"4602_CR41","doi-asserted-by":"crossref","unstructured":"Wang J, Lukasiewicz T, Hu X, Cai J, Xu Z (2021a) Rsg: A simple but effective module for learning imbalanced datasets. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 3784\u20133793","DOI":"10.1109\/CVPR46437.2021.00378"},{"key":"4602_CR42","doi-asserted-by":"crossref","unstructured":"Wang J, Zhang W, Zang Y, Cao Y, Pang J, Gong T, Chen, K, Liu Z, Loy C\u00a0C, Lin D (2021b) Seesaw loss for long-tailed instance segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 9695\u20139704","DOI":"10.1109\/CVPR46437.2021.00957"},{"key":"4602_CR39","doi-asserted-by":"crossref","unstructured":"Wang P, Han K, Wei, X.-S, Zhang L, Wang L (2021c) Contrastive learning based hybrid networks for long-tailed image classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 943\u2013952","DOI":"10.1109\/CVPR46437.2021.00100"},{"key":"4602_CR40","unstructured":"Wang X, Lian L, Miao Z, Liu Z, Yu S (2021e) Long-tailed recognition by routing diverse distribution-aware experts. arXiv:2010.01809"},{"key":"4602_CR43","doi-asserted-by":"crossref","unstructured":"Wang T, Zhu Y, Zhao C, Zeng W, Wang J, Tang M (2021d). Adaptive class suppression loss for long-tail object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 3103\u20133112","DOI":"10.1109\/CVPR46437.2021.00312"},{"key":"4602_CR45","doi-asserted-by":"crossref","unstructured":"Yu S, Guo J, Zhang R, Fan Y, Wang Z, Cheng X (2022). A re-balancing strategy for class-imbalanced classification based on instance difficulty. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 70\u201379","DOI":"10.1109\/CVPR52688.2022.00017"},{"key":"4602_CR46","doi-asserted-by":"crossref","unstructured":"Zhou B, Cui Q, Wei X.-S, Chen Z.-M (2020). Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 9719\u20139728","DOI":"10.1109\/CVPR42600.2020.00974"},{"key":"4602_CR47","doi-asserted-by":"publisher","first-page":"3074","DOI":"10.1109\/TMM.2021.3092571","volume":"24","author":"X Zhu","year":"2022","unstructured":"Zhu X, Guo K, Fang H, Chen L, Ren S, Hu B (2022a) Cross view capture for stereo image super-resolution. IEEE Trans Multimed 24:3074\u20133086","journal-title":"IEEE Trans Multimed"},{"issue":"3","key":"4602_CR48","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1109\/TCSVT.2021.3078436","volume":"32","author":"X Zhu","year":"2022","unstructured":"Zhu X, Guo K, Ren S, Hu B, Hu M, Fang H (2022b) Lightweight image super-resolution with expectation-maximization attention mechanism. IEEE Trans Circuits Syst Video Technol 32(3):1273\u20131284","journal-title":"IEEE Trans Circuits Syst Video Technol"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-023-04602-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12652-023-04602-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-023-04602-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T18:22:56Z","timestamp":1684866176000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12652-023-04602-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,18]]},"references-count":48,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["4602"],"URL":"https:\/\/doi.org\/10.1007\/s12652-023-04602-z","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"value":"1868-5137","type":"print"},{"value":"1868-5145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,18]]},"assertion":[{"value":"28 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2023","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 that they have no conflict of interest in this work. The authors have employed some public datasets, namely, MSCOCO, MNIST, CIFAR10, CIFAR100 for performing the experiments in the considered work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}