{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:26:26Z","timestamp":1760711186311,"version":"3.37.3"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T00:00:00Z","timestamp":1683158400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T00:00:00Z","timestamp":1683158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62141602"],"award-info":[{"award-number":["62141602"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2021J011003"],"award-info":[{"award-number":["2021J011003"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s13042-023-01835-4","type":"journal-article","created":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T11:03:17Z","timestamp":1683198197000},"page":"3323-3336","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Coarse-to-fine knowledge transfer based long-tailed classification via bilateral-sampling network"],"prefix":"10.1007","volume":"14","author":[{"given":"Junyan","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9339-1829","authenticated-orcid":false,"given":"Hong","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,4]]},"reference":[{"key":"1835_CR1","unstructured":"Cao Y, Kuang J, Gao M, Zhou A, Wen Y, Chua T (2023) Learning relation prototype from unlabeled texts for long-tail relation extraction. IEEE Trans Knowl Data Eng 35(2):1761\u20131774"},{"key":"1835_CR2","doi-asserted-by":"crossref","unstructured":"Wu T, Liu Z, Huang Q, Wang Y, Lin D (2021) Adversarial robustness under long-tailed distribution. In: IEEE\/CVF conference on computer vision and pattern recognition, pp 8659\u20138668","DOI":"10.1109\/CVPR46437.2021.00855"},{"issue":"1","key":"1835_CR3","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.cell.2020.03.022","volume":"181","author":"J Goecks","year":"2020","unstructured":"Goecks J, Jalili V, Heiser LM, Gray JW (2020) How machine learning will transform biomedicine. Cell 181(1):92\u2013101","journal-title":"Cell"},{"issue":"7","key":"1835_CR4","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1111\/2041-210X.13608","volume":"12","author":"R Kulkarni","year":"2021","unstructured":"Kulkarni R, Di Minin E (2021) Automated retrieval of information on threatened species from online sources using machine learning. Methods Ecol Evol 12(7):1226\u20131239","journal-title":"Methods Ecol Evol"},{"issue":"6","key":"1835_CR5","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1049\/bme2.12029","volume":"10","author":"D Zeng","year":"2021","unstructured":"Zeng D, Veldhuis R, Spreeuwers L (2021) A survey of face recognition techniques under occlusion. IET Biom 10(6):581\u2013606","journal-title":"IET Biom"},{"issue":"1","key":"1835_CR6","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1007\/s11069-021-04620-0","volume":"107","author":"M Haggag","year":"2021","unstructured":"Haggag M, Siam AS, El-Dakhakhni W, Coulibaly P, Hassini E (2021) A deep learning model for predicting climate-induced disasters. Nat Hazards 107(1):1009\u20131034","journal-title":"Nat Hazards"},{"key":"1835_CR7","doi-asserted-by":"crossref","unstructured":"Lin T, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: IEEE international conference on computer vision, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"1835_CR8","first-page":"1","volume":"32","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. Adv Neural Inf Process Syst 32:1\u20138","journal-title":"Adv Neural Inf Process Syst"},{"key":"1835_CR9","doi-asserted-by":"crossref","unstructured":"Jamal MA, Brown M, Yang M, Wang L, Gong B (2020) Rethinking class-balanced methods for long-tailed visual recognition from a domain adaptation perspective. In: IEEE\/CVF conference on computer vision and pattern recognition, pp 7610\u20137619","DOI":"10.1109\/CVPR42600.2020.00763"},{"issue":"11","key":"1835_CR10","doi-asserted-by":"publisher","first-page":"2781","DOI":"10.1109\/TPAMI.2019.2914680","volume":"42","author":"C Huang","year":"2019","unstructured":"Huang C, Li Y, Loy CC, Tang X (2019) Deep imbalanced learning for face recognition and attribute prediction. IEEE Trans Pattern Anal Mach Intell 42(11):2781\u20132794","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1835_CR11","doi-asserted-by":"crossref","unstructured":"Xiang L, Ding G, Han J (2020) Learning from multiple experts: self-paced knowledge distillation for long-tailed classification. In: European conference on computer vision, pp 247\u2013263","DOI":"10.1007\/978-3-030-58558-7_15"},{"key":"1835_CR12","doi-asserted-by":"crossref","unstructured":"Chu P, Bian X, Liu S, Ling H (2020) Feature space augmentation for long-tailed data. In: European conference on computer vision, pp 694\u2013710","DOI":"10.1007\/978-3-030-58526-6_41"},{"key":"1835_CR13","doi-asserted-by":"crossref","unstructured":"Yin X, Yu X, Sohn K, Liu X, Chandraker M (2019) Feature transfer learning for face recognition with under-represented data. In: IEEE\/CVF conference on computer vision and pattern recognition, pp 5704\u20135713","DOI":"10.1109\/CVPR.2019.00585"},{"issue":"2","key":"1835_CR14","doi-asserted-by":"publisher","first-page":"194","DOI":"10.3390\/sym13020194","volume":"13","author":"Z Jiang","year":"2021","unstructured":"Jiang Z, Pan T, Zhang C, Yang J (2021) A new oversampling method based on the classification contribution degree. Symmetry 13(2):194","journal-title":"Symmetry"},{"issue":"2","key":"1835_CR15","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1109\/TKDE.2012.232","volume":"26","author":"S Barua","year":"2012","unstructured":"Barua S, Islam MM, Yao X, Murase K (2012) MWMOTE-majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans Knowl Data Eng 26(2):405\u2013425","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1835_CR16","doi-asserted-by":"crossref","unstructured":"Han H, Wang W, Mao B (2005) Borderline-smote: a new over-sampling method in imbalanced data sets learning. In: International conference on intelligent computing, pp 878\u2013887","DOI":"10.1007\/11538059_91"},{"key":"1835_CR17","doi-asserted-by":"crossref","unstructured":"Kim J, Jeong J, Shin J (2020) M2m: imbalanced classification via major-to-minor translation. In: IEEE\/CVF conference on computer vision and pattern recognition, pp 13896\u201313905","DOI":"10.1109\/CVPR42600.2020.01391"},{"issue":"11","key":"1835_CR18","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1109\/TCYB.2014.2372060","volume":"45","author":"WW Ng","year":"2014","unstructured":"Ng WW, Hu J, Yeung DS, Yin S, Roli F (2014) Diversified sensitivity-based undersampling for imbalance classification problems. IEEE Trans Cybern 45(11):2402\u20132412","journal-title":"IEEE Trans Cybern"},{"key":"1835_CR19","doi-asserted-by":"crossref","unstructured":"Deng X, Zhong W, Ren J, Zeng D, Zhang H (2016) An imbalanced data classification method based on automatic clustering under-sampling. In: IEEE International performance computing and communications conference, pp 1\u20138","DOI":"10.1109\/PCCC.2016.7820640"},{"issue":"12","key":"1835_CR20","doi-asserted-by":"publisher","first-page":"4263","DOI":"10.1109\/TCYB.2016.2606104","volume":"47","author":"Q Kang","year":"2016","unstructured":"Kang Q, Chen X, Li S (2016) A noise-filtered under-sampling scheme for imbalanced classification. IEEE Trans Cybern 47(12):4263\u20134274","journal-title":"IEEE Trans Cybern"},{"issue":"2","key":"1835_CR21","first-page":"55","volume":"16","author":"G Rekha","year":"2020","unstructured":"Rekha G, Reddy VK, Tyagi AK (2020) Critical instances removal based under-sampling (CIRUS): a solution for class imbalance problem. Int J Hybrid Intell Syst 16(2):55\u201366","journal-title":"Int J Hybrid Intell Syst"},{"key":"1835_CR22","doi-asserted-by":"crossref","unstructured":"Xu H, Zhang X, Li H, Xie L, Dai W, Xiong H, Tian Q (2022) Seed the views: hierarchical semantic alignment for contrastive representation learning. IEEE Trans Pattern Anal Mach Intell 45(3):3753\u20133767","DOI":"10.1109\/TPAMI.2022.3176690"},{"key":"1835_CR23","doi-asserted-by":"crossref","unstructured":"Li S, Gong K, Liu CH, Wang Y, Qiao F, Cheng X (2021) Metasaug: meta semantic augmentation for long-tailed visual recognition. In: IEEE\/CVF conference on computer vision and pattern recognition, pp 5212\u20135221","DOI":"10.1109\/CVPR46437.2021.00517"},{"issue":"1","key":"1835_CR24","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/TETCI.2022.3171784","volume":"7","author":"W Xu","year":"2023","unstructured":"Xu W, Yuan K, Li W, Ding W (2023) An emerging fuzzy feature selection method using composite entropy-based uncertainty measure and data distribution. IEEE Trans Emerg Top Comput Intell 7(1):76\u201388","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"issue":"2","key":"1835_CR25","first-page":"447","volume":"57","author":"J Li","year":"2020","unstructured":"Li J, Li Y, Mi Y, Wu W (2020) Meso-granularity labeled method for multi-granularity formal concept analysis. J Comput Res Dev 57(2):447\u2013458","journal-title":"J Comput Res Dev"},{"key":"1835_CR26","doi-asserted-by":"crossref","unstructured":"Liu R (2022) A novel synthetic minority oversampling technique based on relative and absolute densities for imbalanced classification. Appl Intell 53(1):768\u2013803","DOI":"10.1007\/s10489-022-03512-5"},{"key":"1835_CR27","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":"1835_CR28","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"},{"issue":"10","key":"1835_CR29","doi-asserted-by":"publisher","first-page":"3738","DOI":"10.1016\/j.patcog.2012.03.014","volume":"45","author":"MA Tahir","year":"2012","unstructured":"Tahir MA, Kittler J, Yan F (2012) Inverse random under sampling for class imbalance problem and its application to multi-label classification. Pattern Recognit 45(10):3738\u20133750","journal-title":"Pattern Recognit"},{"key":"1835_CR30","doi-asserted-by":"crossref","unstructured":"Zhou B, Cui Q, Wei X, Chen Z (2020) Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition. In: IEEE\/CVF conference on computer vision and pattern recognition, pp 9719\u20139728","DOI":"10.1109\/CVPR42600.2020.00974"},{"key":"1835_CR31","doi-asserted-by":"crossref","unstructured":"Yao Y (2004) A partition model of granular computing. LNCS Trans Rough Sets I, LNCS 3100:232\u2013253","DOI":"10.1007\/978-3-540-27794-1_11"},{"key":"1835_CR32","doi-asserted-by":"crossref","unstructured":"Yao Y (2008) Granular computing: past, present and future. In: IEEE international conference on granular computing, pp 80\u201385","DOI":"10.1007\/978-3-540-79721-0_8"},{"key":"1835_CR33","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1016\/j.neucom.2021.07.008","volume":"459","author":"Q Chen","year":"2021","unstructured":"Chen Q, Liu Q, Lin E (2021) A knowledge-guide hierarchical learning method for long-tailed image classification. Neurocomputing 459:408\u2013418","journal-title":"Neurocomputing"},{"key":"1835_CR34","doi-asserted-by":"crossref","unstructured":"Xu W, Guo D, Qian Y, Ding W (2022) Two-way concept-cognitive learning method: a fuzzy-based progressive learning. IEEE Trans Fuzzy Syst 1\u201315","DOI":"10.1109\/TNNLS.2023.3235800"},{"key":"1835_CR35","unstructured":"Xu W, Pan Y, Chen X, Ding W, Qian Y (2022) A novel dynamic fusion approach using information entropy for interval-valued ordered datasets. IEEE Trans Big Data 1\u201315"},{"key":"1835_CR36","unstructured":"Kendall A, Gal Y, Cipolla R (2018) Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: IEEE conference on computer vision and pattern recognition, pp 7482\u20137491"},{"key":"1835_CR37","doi-asserted-by":"crossref","unstructured":"Li T, Wang L, Wu G (2021) Self supervision to distillation for long-tailed visual recognition. In: IEEE\/CVF international conference on computer vision, pp 630\u2013639","DOI":"10.1109\/ICCV48922.2021.00067"},{"key":"1835_CR38","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: European conference on computer vision, pp 630\u2013645","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"1835_CR39","doi-asserted-by":"crossref","unstructured":"Bottou L (2012) Stochastic gradient descent tricks. In: Montavon G, Orr GB, M\u00fcller KR (eds) Neural networks: tricks of the trade. Springer, Berlin, pp 421\u2013436","DOI":"10.1007\/978-3-642-35289-8_25"},{"key":"1835_CR40","doi-asserted-by":"crossref","unstructured":"Xu W, Guo D, Mi J, Qian Y, Zheng K, Ding W (2023) Two-way concept-cognitive learning via concept movement viewpoint. IEEE Trans Neural Netw Learn Syst 1\u201315","DOI":"10.1109\/TNNLS.2023.3235800"},{"issue":"11","key":"1835_CR41","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/219717.219748","volume":"38","author":"GA Miller","year":"1995","unstructured":"Miller GA (1995) WordNet: a lexical database for English. Commun ACM 38(11):39\u201341","journal-title":"Commun ACM"},{"key":"1835_CR42","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.ins.2021.10.058","volume":"584","author":"K Yuan","year":"2022","unstructured":"Yuan K, Xu W, Li W, Ding W (2022) An incremental learning mechanism for object classification based on progressive fuzzy three-way concept. Inf Sci 584:127\u2013147","journal-title":"Inf Sci"},{"key":"1835_CR43","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"2","key":"1835_CR44","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The Pascal visual object classes (VOC) challenge. Int J Comput Vis 88(2):303\u2013338","journal-title":"Int J Comput Vis"},{"issue":"10","key":"1835_CR45","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1167\/17.10.296","volume":"17","author":"B Zhou","year":"2017","unstructured":"Zhou B, Lapedriza A, Torralba A, Oliva A (2017) Places: an image database for deep scene understanding. J Vis 17(10):296\u2013296","journal-title":"J Vis"},{"key":"1835_CR46","doi-asserted-by":"crossref","unstructured":"Cui Y, Song Y, Sun C, Howard A, Belongie S (2018) Large scale fine-grained categorization and domain-specific transfer learning. In: IEEE conference on computer vision and pattern recognition, pp 4109\u20134118","DOI":"10.1109\/CVPR.2018.00432"},{"key":"1835_CR47","doi-asserted-by":"crossref","unstructured":"Huang C, Li Y, Loy CC, Tang X (2016) Learning deep representation for imbalanced classification. In: IEEE conference on computer vision and pattern recognition, pp 5375\u20135384","DOI":"10.1109\/CVPR.2016.580"},{"issue":"1","key":"1835_CR48","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","volume":"134","author":"P De Boer","year":"2005","unstructured":"De Boer P, Kroese DP, Mannor S, Rubinstein RY (2005) A tutorial on the cross-entropy method. Ann Oper Res 134(1):19\u201367","journal-title":"Ann Oper Res"},{"key":"1835_CR49","doi-asserted-by":"publisher","first-page":"900","DOI":"10.1016\/j.ins.2022.07.015","volume":"608","author":"Z Li","year":"2022","unstructured":"Li Z, Zhao H, Lin Y (2022) Multi-task convolutional neural network with coarse-to-fine knowledge transfer for long-tailed classification. Inf Sci 608:900\u2013916","journal-title":"Inf Sci"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01835-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-01835-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01835-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T05:24:56Z","timestamp":1692681896000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-01835-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,4]]},"references-count":49,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["1835"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-01835-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2023,5,4]]},"assertion":[{"value":"19 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 April 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 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 known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}