深度长尾学习研究综述

韩佳艺; 刘建伟; 陈德华; 徐璟东; 代琪; 夏鹏飞

doi:10.16383/j.aas.c240077

深度长尾学习研究综述

doi: 10.16383/j.aas.c240077 cstr: 32138.14.j.aas.c240077

1.
中国石油大学(北京)人工智能学院自动化系北京 102249
2.
东华大学计算机科学与技术学院上海 201620

详细信息

作者简介:
韩佳艺：中国石油大学(北京)人工智能学院自动化系博士研究生. 主要研究方向为深度长尾学习与计算机视觉. E-mail: 864494560@qq.com

刘建伟：中国石油大学(北京)人工智能学院自动化系副教授. 2006年获得东华大学博士学位. 主要研究方向为智能系统, 机器学习, 复杂非线性系统的分析、预测与控制. E-mail: liujw@cup.edu.cn

陈德华：东华大学计算机科学与技术学院教授. 主要研究方向为数据科学, 深度学习等. 本文通信作者. E-mail: chendehua@dhu.edu.cn

徐璟东：中国石油大学(北京)人工智能学院自动化系硕士研究生. 主要研究方向为深度长尾学习, 因果推断. E-mail: 2948473452@qq.com

代琪：中国石油大学(北京)人工智能学院自动化系博士. 2024年毕业于中国石油大学信息科学与工程学院自动化系控制理论与控制工程专业获博士学位. 主要研究方向为数据挖掘和机器学习

夏鹏飞：东华大学计算机科学与技术学院博士研究生. E-mail: x6635570@163.com

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出版历程
- 收稿日期: 2024-02-04
- 录用日期: 2024-07-23
- 网络出版日期: 2024-10-24

Survey on deep long-tailed learning

1.
College of Artificial Intelligence, China University of Petroleum-Beijing, Beijing 102249
2.
College of Computer Science and Technology, Donghua University, Shanghai 201620

More Information

Author Bio:
HAN Jia-Yi　Ph. D.candidate at the Department of Automation, college of Artificial Intelligence, China University of Petroleum, Beijing. Her research interests include deep long-tailed learning and computer vision

LIU Jian-Wei　Associate Professor at the Department of Automation, college of Artificial Intelligence, China University of Petroleum, Beijing. He received the Ph.D. degree in control theory and control engineering from DongHua University in 2006. His research interests include pattern recognition and intelligent Systems, machine learning, analysis, prediction and control of complex non-linear system

CHEN De-Hua　Professor at the Department of computer science and technology, Donghua University. His research interests include data science and deep learning. Corresponding author of this paper

XU Jing-Dong　Master student at the Department of Automation, college of Artificial Intelligence, China University of Petroleum, Beijing. His research interests include deep long-tailed learning and causal inference

Dai Qi　Ph. D. at the Department of Automation, college of Artificial Intelligence, China University of Petroleum, Beijing. He received his Ph.D. degree in Control Theory and Control Engineering from China University of Petroleum, Beijing in 2024. His research interests include data mining and machine learning

CHEN De-Hua　Ph. D.candidate at the Department of computer science and technology, Donghua University

摘要

摘要: 深度学习是一门依赖于数据的科学, 传统深度学习方法假定在平衡数据集上训练模型, 然而, 现实世界中大规模数据集通常表现出长尾分布现象, 样本数量众多的少量头部类主导模型训练, 而大量尾部类样本数量过少, 难以得到充分学习. 近年来, 长尾学习掀起学术界的研究热潮, 涌现出大量先进的工作. 本文综合梳理和分析了近年来发表在高水平会议或期刊上的文献, 对长尾学习进行全面的综述. 具体而言, 根据深度学习模型设计流程, 将图像识别领域的长尾学习算法分为丰富样本数量与语义信息的优化样本空间方法, 关注特征提取器、分类器、logits和损失函数这四个基本组成部分的优化模型方法以及通过引入帮助模型训练的辅助任务, 在多个空间共同优化长尾学习模型的辅助任务学习3大类, 并根据提出的分类方法综合对比分析每类长尾学习方法的优缺点. 然后, 进一步将基于样本数量的狭义长尾学习概念推广至多尺度广义长尾学习. 此外, 本文对文本数据、语音数据等其它数据形式下的长尾学习算法进行简要评述. 最后, 讨论了目前长尾学习面临的可解释性较差、数据质量较低等挑战, 并展望了如多模态长尾学习、半监督长尾学习等未来具有潜力的发展方向.
- 深度长尾学习 /
- 长尾分布 /
- 不平衡学习 /
- 深度学习
Abstract: Deep learning is a science that depends on data. Traditional deep learning methods unrealistically assume that the training models are on balanced datasets. In real-world large-scale datasets, a long-tailed distribution often occurs, with a few head classes having many samples dominating model training, while many tail classes have too few samples to be adequately learned. In recent years, the long-tailed learning has set off a research upsurge in academic circles. In this paper, we synthesize and analyze the literature published in high-level conferences or journals to provide a comprehensive survey of long-tailed learning. Specifically, we categorize long-tailed learning algorithms in the field of image recognition according to the design process of deep learning models into three main types: optimizing the sample space by enriching the quantity and semantic information of samples, optimizing the model by focusing on the four fundamental components of feature extractor, classifier, logits, and loss function, and auxiliary task learning, which involves introducing auxiliary tasks to aid model training and jointly optimizing long-tailed learning models across multiple spaces. Additionally, a comprehensive comparative analysis of the strengths and weaknesses of each category is conducted based on the proposed classification method. We further extend the concept of narrow long-tail learning based on the number of samples to multi-scale generalized long-tailed learning. In addition, we briefly review long-tailed learning algorithms in other data forms such as text data. Finally, we discussed the current challenges faced by long-tailed learning, such as poor interpretability and low data quality, and explored promising future directions such as multimodal long-tailed learning and semi-supervised long-tailed learning.
- Deep long-tailed learning /
- Long-tailed distribution /
- Imbalanced learning /
- Deep learning

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图 1 深度长尾学习研究综述组织结构图

Fig. 1 Organizational Structure Diagram of a Survey on Deep Long-Tail Learning Research

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图 2 长尾训练集示意图

Fig. 2 Illustration of Long-Tail Training Set

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图 3 长尾测试集示意图

Fig. 3 Illustration of Long-Tail Testing Set

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图 4 常用长尾数据集分布

Fig. 4 Distributions of Common Long-Tail Datasets

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图 6 神经网络结构示意图

Fig. 6 Diagram of Neural Network Architecture

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图 5 长尾图像识别研究现状

Fig. 5 Current Status of Long-Tail Image Recognition Research

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图 8 优化样本空间各方法关系示意图

Fig. 8 Diagram of Relationships Among Various Methods for Optimizing Sample Space

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图 7 重采样示意图

Fig. 7 Diagram of Resampling

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图 9 单样本变换示意图

Fig. 9 Diagram of Single Sample Transformation

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图 10 多样本变换方法示意图

Fig. 10 Diagram of Multiple Sample Transformation Methods

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图 11 背景增强示意图

Fig. 11 Background Enhancement Diagram

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图 12 语义增强样例图

Fig. 12 Example Diagram of Semantic Enhancement

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图 13 优化模型空间

Fig. 13 Optimized Model Space

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图 14 辅助任务学习各方法关系示意图

Fig. 14 Diagram of the Relationships Among Various Methods in Auxiliary Task Learning

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图 15 两阶段解耦学习模型示意图^[1]

Fig. 15 Diagram of the Two-Stage Decoupled Learning Model^[1]

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图 16 双分支网络(Bilateral-Branch Network, BBN)结构示意图^[14]

Fig. 16 Diagram of the Bilateral-Branch Network (BBN) Architecture^[14]

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图 17 Range loss示意图^[40]

Fig. 17 Diagram of Range Loss^[40]

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图 18 三阶段长尾知识蒸馏模型^[147]

Fig. 18 Three-Stage Long-Tail Knowledge Distillation Model^[147]

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图 19 长尾集成学习模型示意图

Fig. 19 Diagram of Long-Tail Ensemble Learning Model

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图 20 类间样本数量长尾分布与类内属性长尾分布示例图

Fig. 20 Example Diagram of Long-Tail Distribution of Inter-Class Sample Counts and Intra-Class Attributes

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表 1 常见长尾数据集基本信息

Table 1 Basic Information of Common Long-Tail Datasets

类型	数据集	类别数量	训练集样本数量	测试集样本数量	最大类样本数量	最小类样本数量
图像分类	CIFAR10-LT^[13]	10	50000	10000	5000	5($ \rho$=100), 50($ \rho$=10)
图像分类	CIFAR100-LT^[13]	100	50000	10000	500	5($ \rho$=100), 50($ \rho$=10)
目标检测	ImageNet-LT^[62]	1000	115846	50000	1280	5
场景识别	Places-LT^[62]	365	62500	36500	4980	5
人脸识别	MS1M-LT^[62]	74500(ID)	887530	3530	598	1
目标检测	iNaturalist2017^[63]	5089	579184	182707	196613	381
目标检测	iNaturalist 2018^[63]	8142	437513	24426	127551	19
实例分割	LVIS v0.5^[64]	1230	57000	20000	26148	1
实例分割	LVIS v1^[64]	1203	100170	19822	50552	1
场景理解	SUN-LT^[65]	397	4084	2868	12	2
目标检测	AWA-LT^[65]	50	6713	6092	720	2
鸟类识别	CUB-LT^[65]	200	2945	2348	43	2或3
图像分类	STL10-LT^[66]	10	5000	8000	500	5($ \rho$=100), 50($ \rho$=10)
目标检测	VOC-LT^[67]	20	1142	4952	775	4
视频分类	VideoLT^[68]	1004	179352	51244	1912	44

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表 2 长尾图像识别方法比较

Table 2 Comparison of Long-Tail Image Recognition Methods

分类		代表性文献	优点	缺点
优化样本空间	重采样	[1, 2, 56, 80, 70, 82, 30, 169]	简单通用, 理论直观, 易于操作	1)会丢弃大量头部类有效信息 2)重复采样尾部类不能增加有效信息, 并容易引发过拟合 3)易引入其它噪声
优化样本空间	数据增强	[2, 8, 9, 15, 76, 88, 89, 94, 95]	样本变换法成本较低, 易与其它方法结合, 灵活性较高. 语义增强法丰富尾部样本的语义信息, 生成具有现实意义的新样本	1)样本变换法引入大量新数据, 增加模型训练成本, 且可能生成毫无意义的样本, 鲁棒性较差. 2)语义增强方法需设计专门的模型结构, 操作复杂. 并过于依赖于头部类数据质量, 易给模型带来新的偏置.
优化模型空间	优化特征提取器	[107, 108, 109, 111, 112, 170]	有效增强样本上下文语义特征帮助模型学到无偏的特征表示	1)引入大量参数, 占用内存, 降低训练效率 2)可解释性较差
	优化分类器	[1, 16, 26, 113, 115, 116, 118, 119]	计算开销小, 训练稳定无需设计额外的损失函数或存储单元	1)对超参数和优化器的选择敏感, 试错代价高 2)灵活性较低, 在目标检测与实例分割任务上表现不佳
	logits调整	[12, 28, 30, 55, 71, 120, 122]	既能优化训练过程, 又能进行事后修正. 计算开销较低, 泛化性能良好, 易与其它方法结合.	1)依赖于数据集的先验分布 2)修正后的边际分布可能不满足期望分布.
	代价敏感加权损失函数	[11, 12, 54, 72, 127, 129, 133]	操作简单, 易于实现, 计算开销较小, 适应于实际应用场景	1)优化困难, 参数敏感, 难以处理大规模真实场景 2)头尾性能像“跷跷板”, 无法从本质上解决信息缺乏的问题
辅助任务学习	解耦学习	[1, 14, 134, 135, 138, 139]	利用大量头部类数据生成泛化能力良好的特征表示能够有效提升模型性能, 且计算成本较低.	1)两阶段方法不利于端到端的模型训练与部署 2)对数据依赖性较强 3)与其它算法结合使用时需重新设计, 实用性不强
	度量学习	[40, 58, 59, 127, 145, 149, 151]	便于公式化与计算构建一个正样本接近, 负样本远离的特征空间, 优化决策边界.	1)尾部类样本极少的情况下性能很差. 2)依赖于度量损失函数的设计
	知识蒸馏	[17, 19, 36, 145, 147, 154]	重用模型资源, 充分利用数据集蕴含的知识. 稳定尾部类学习过程	1)计算开销大, 优化成本相对过高, 对超参数敏感 2)易出现师生不匹配问题, 整体性能过于依赖教师模型的学习情况
	集成学习	[18, 19, 20, 158, 159, 161]	在头部类和尾部类上都能保持良好性能泛化能力良好, 能够处理未知分布的测试集	1)计算和存储负担过大, 框架部署复杂 2)专家之间存在相互影响的情况, 难以有效整合专家
	层次学习	[23, 24, 25, 162]	对数据间的关系进行多粒度建模, 捕捉类间隐式语义关系有助于头尾知识迁移	1)模型设计复杂, 训练成本较高 2)依赖于高质量数据, 有时需要数据集提供外部信息 3)层次划分步骤对后续训练产生过大影响

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参考文献(210)

[1]	Kang B, Xie S, Rohrbach M, et al. Decoupling representation and classifier for long-tailed recognition[J]. arXiv preprint arXiv: 1910.09217, 2019.
[2]	Zhang Y, Wei X S, Zhou B, et al. Bag of tricks for long-tailed visual recognition with deep convolutional neural networks[C]//Proceedings of the AAAI conference on artificial intelligence. 2021, 35(4): 3447−3455.
[3]	Wang J, Zhang W, Zang Y, et al. Seesaw loss for long-tailed instance segmentation[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 9695−9704.
[4]	Fu Y, Xiang L, Zahid Y, et al. Long-tailed visual recognition with deep models: A methodological survey and evaluation. Neurocomputing, 2022
[5]	Yang L, Jiang H, Song Q, et al. A survey on long-tailed visual recognition. International Journal of Computer Vision, 2022, 130(7): 1837−1872 doi: 10.1007/s11263-022-01622-8
[6]	Drummond C, Holte R C. C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling[C]//Workshop on learning from imbalanced datasets II. 2003, 11: 1−8.
[7]	Shen L, Lin Z, Huang Q. Relay backpropagation for effective learning of deep convolutional neural networks[C]//Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part VII 14. Springer International Publishing, 2016: 467−482.
[8]	Chou H P, Chang S C, Pan J Y, et al. Remix: rebalanced mixup[C]//Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings, Part VI 16. Springer International Publishing, 2020: 95−110.
[9]	Kim J, Jeong J, Shin J. M2m: Imbalanced classification via major-to-minor translation[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 13896−13905.
[10]	Chu P, Bian X, Liu S, et al. Feature space augmentation for long-tailed data[C]//Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16. Springer International Publishing, 2020: 694−710.
[11]	Cui Y, Jia M, Lin T Y, et al. Class-balanced loss based on effective number of samples[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019: 9268−9277.
[12]	Tan J, Wang C, Li B, et al. Equalization loss for long-tailed object recognition[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 11662−11671.
[13]	Cao K, Wei C, Gaidon A, et al. Learning imbalanced datasets with label-distribution-aware margin loss. Advances in neural information processing systems, 201932
[14]	Zhou B, Cui Q, Wei X S, et al. Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 9719−9728.
[15]	Zhou A, Tajwar F, Robey A, et al. Do deep networks transfer invariances across classes?[J]. arXiv preprint arXiv: 2203.09739, 2022.
[16]	Liu B, Li H, Kang H, et al. Gistnet: a geometric structure transfer network for long-tailed recognition[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2021: 8209−8218.
[17]	Zhang S, Chen C, Hu X, et al. Balanced knowledge distillation for long-tailed learning. Neurocomputing, 2023, 527: 36−46 doi: 10.1016/j.neucom.2023.01.063
[18]	Sharma S, Yu N, Fritz M, et al. Long-tailed recognition using class-balanced experts[C]//Pattern Recognition: 42nd DAGM German Conference, DAGM GCPR 2020, Tübingen, Germany, September 28–October 1, 2020, Proceedings 42. Springer International Publishing, 2021: 86−100.
[19]	Xiang L, Ding G, Han J. Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification[C]//Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part V 16. Springer International Publishing, 2020: 247−263.
[20]	Cai J, Wang Y, Hwang J N. Ace: Ally complementary experts for solving long-tailed recognition in one-shot[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021: 112−121.
[21]	Cai J, Wang Y, Hsu H M, et al. Luna: Localizing unfamiliarity near acquaintance for open-set long-tailed recognition[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2022, 36(1): 131−139.
[22]	Liu X, Zhang J, Hu T, et al. Inducing Neural Collapse in Deep Long-tailed Learning[C]//International Conference on Artificial Intelligence and Statistics. PMLR, 2023: 11534−11544.
[23]	Wu J, Song L, Zhang Q, et al. Forestdet: Large-vocabulary long-tailed object detection and instance segmentation. IEEE Transactions on Multimedia, 2021, 24: 3693−3705
[24]	Ouyang W, Wang X, Zhang C, et al. Factors in finetuning deep model for object detection with long-tail distribution[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 864−873.
[25]	Li B. Adaptive Hierarchical Representation Learning for Long-Tailed Object Detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 2313−2322.
[26]	Tang K, Huang J, Zhang H. Long-tailed classification by keeping the good and removing the bad momentum causal effect. Advances in Neural Information Processing Systems, 2020, 33: 1513−1524
[27]	Zhu B, Niu Y, Hua X S, et al. Cross-domain empirical risk minimization for unbiased long-tailed classification[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2022, 36(3): 3589−3597.
[28]	Wu T, Liu Z, Huang Q, et al. Adversarial robustness under long-tailed distribution[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 8659−8668.
[29]	Wang Y X, Ramanan D, Hebert M. Learning to model the tail. Advances in neural information processing systems, 201730
[30]	Ren J, Yu C, Ma X, et al. Balanced meta-softmax for long-tailed visual recognition. Advances in neural information processing systems, 2020, 33: 4175−4186
[31]	Dong B, Zhou P, Yan S, et al. Lpt: Long-tailed prompt tuning for image classification[J]. arXiv preprint arXiv: 2210.01033, 2022.
[32]	Tang K, Tao M, Qi J, et al. Invariant feature learning for generalized long-tailed classification[C]//European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022: 709−726.
[33]	Zhang R, Haihong E, Yuan L, et al. MBNM: multi-branch network based on memory features for long-tailed medical image recognition. Computer Methods and Programs in Biomedicine, 2021, 212: 106448 doi: 10.1016/j.cmpb.2021.106448
[34]	Ju L, Yu Z, Wang L, et al. Hierarchical Knowledge Guided Learning for Real-world Retinal Disease Recognition. IEEE Transactions on Medical Imaging, 2023
[35]	Yang Z, Pan J, Yang Y, et al. Proco: Prototype-aware contrastive learning for long-tailed medical image classification[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2022: 173−182.
[36]	Zhao W, Liu J, Liu Y, et al. Teaching teachers first and then student: Hierarchical distillation to improve long-tailed object recognition in aerial images. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1−12
[37]	Li G, Pan L, Qiu L, et al. A Two-Stage Shake-Shake Network for Long-Tailed Recognition of SAR Aerial View Objects[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 249−256.
[38]	Jiao W, Zhang J. Sonar Images Classification While Facing Long-Tail and Few-Shot. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1−20
[39]	Guo S, Liu R, Wang M, et al. Exploiting the Tail Data for Long-Tailed Face Recognition. IEEE Access, 2022, 10: 97945−97953 doi: 10.1109/ACCESS.2022.3206040
[40]	Zhang X, Fang Z, Wen Y, et al. Range loss for deep face recognition with long-tail[J]. arXiv preprint arXiv: 1611.08976, 2016.
[41]	Moon W J, Seong H S, Heo J P. Minority-Oriented Vicinity Expansion with Attentive Aggregation for Video Long-Tailed Recognition[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2023, 37(2): 1931−1939.
[42]	Zhang C, Ren L, Wang J, et al. Making Pre-trained Language Models Good Long-tailed Learners[J]. arXiv preprint arXiv: 2205.05461, 2022.
[43]	Li Y, Shen T, Long G, et al. Improving long-tail relation extraction with collaborating relation-augmented attention[J]. arXiv preprint arXiv: 2010.03773, 2020.
[44]	Huang Y, Giledereli B, Köksal A, et al. Balancing methods for multi-label text classification with long-tailed class distribution[J]. arXiv preprint arXiv: 2109.04712, 2021.
[45]	Li X, Sun X, Meng Y, et al. Dice loss for data-imbalanced NLP tasks[J]. arXiv preprint arXiv: 1911.02855, 2019.
[46]	Conde M V, Choi U J. Few-shot long-tailed bird audio recognition[J]. arXiv preprint arXiv: 2206.11260, 2022.
[47]	Chen Z, Chen J, Xie Z, et al. Multi-expert Attention Network with Unsupervised Aggregation for long-tailed fault diagnosis under speed variation. Knowledge-Based Systems, 2022, 252: 109393 doi: 10.1016/j.knosys.2022.109393
[48]	Sreepada R S, Patra B K. Mitigating long tail effect in recommendations using few shot learning technique. Expert Systems with Applications, 2020, 140: 112887 doi: 10.1016/j.eswa.2019.112887
[49]	Chaudhary A, Gupta H P, Shukla K K. Real-Time Activities of Daily Living Recognition Under Long-Tailed Class Distribution. IEEE Transactions on Emerging Topics in Computational Intelligence, 2022, 6(4): 740−750 doi: 10.1109/TETCI.2022.3150757
[50]	Zhang Y, Kang B, Hooi B, et al. Deep long-tailed learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
[51]	Pareto V. Cours d'économie politique[M]. Librairie Droz, 1964.
[52]	Zipf G K. The meaning-frequency relationship of words. The Journal of general psychology, 1945, 33(2): 251−256 doi: 10.1080/00221309.1945.10544509
[53]	Hitt M A. The long tail: Why the future of business is selling less of more[J]. 2007.
[54]	Tan J, Lu X, Zhang G, et al. Equalization loss v2: A new gradient balance approach for long-tailed object detection[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 1685−1694.
[55]	Zhang S, Li Z, Yan S, et al. Distribution alignment: A unified framework for long-tail visual recognition[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 2361−2370.
[56]	Sinha S, Ohashi H, Nakamura K. Class-difficulty based methods for long-tailed visual recognition. International Journal of Computer Vision, 2022, 130(10): 2517−2531 doi: 10.1007/s11263-022-01643-3
[57]	Cui J, Zhong Z, Liu S, et al. Parametric contrastive learning[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2021: 715−724.
[58]	Kang B, Li Y, Xie S, et al. Exploring balanced feature spaces for representation learning[C]//International Conference on Learning Representations. 2020.
[59]	Li T, Cao P, Yuan Y, et al. Targeted supervised contrastive learning for long-tailed recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 6918−6928.
[60]	叶志飞, 文益民, 吕宝粮. 不平衡分类问题研究综述. 智能系统学报, 2009, 4(002): 148−156 doi: 10.3969/j.issn.1673-4785.2009.02.010
[61]	赵凯琳, 靳小龙, 王元卓. 小样本学习研究综述. 软件学报, 2020, 32(2): 349−369
[62]	Liu Z, Miao Z, Zhan X, et al. Large-scale long-tailed recognition in an open world[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019: 2537−2546.
[63]	Van Horn G, Mac Aodha O, Song Y, et al. The inaturalist species classification and detection dataset[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 8769−8778.
[64]	Gupta A, Dollar P, Girshick R. Lvis: A dataset for large vocabulary instance segmentation[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019: 5356−5364.
[65]	Samuel D, Atzmon Y, Chechik G. From generalized zero-shot learning to long-tail with class descriptors[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2021: 286−295.
[66]	Oh, Y., Kim, D. J., & Kweon, I. S. (2022). Daso: Distribution-aware semantics-oriented pseudo-label for imbalanced semi-supervised learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9786−9796).
[67]	Wu T, Huang Q, Liu Z, et al. Distribution-balanced loss for multi-label classification in long-tailed datasets[C]//Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IV 16. Springer International Publishing, 2020: 162−178.
[68]	Zhang X, Wu Z, Weng Z, et al. Videolt: Large-scale long-tailed video recognition[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021: 7960−7969.
[69]	Feng C, Zhong Y, Huang W. Exploring classification equilibrium in long-tailed object detection[C]//Proceedings of the IEEE/CVF International conference on computer vision. 2021: 3417−3426.
[70]	Shrivastava A, Gupta A, Girshick R. Training region-based object detectors with online hard example mining[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 761−769.
[71]	Zhao Y, Chen W, Tan X, et al. Adaptive logit adjustment loss for long-tailed visual recognition[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2022, 36(3): 3472−3480.
[72]	Chen Z, Casser V, Kretzschmar H, et al. GradTail: learning long-tailed data using gradient-based sample weighting[J]. arXiv preprint arXiv: 2201.05938, 2022.
[73]	Wah C, Branson S, Welinder P, et al. The caltech-ucsd birds-200-2011 dataset[J]. 2011.
[74]	Zhou B, Lapedriza A, Khosla A, et al. Places: A 10 million image database for scene recognition. IEEE transactions on pattern analysis and machine intelligence, 2017, 40(6): 1452−1464
[75]	Coates A, Ng A, Lee H. An analysis of single-layer networks in unsupervised feature learning[C]//Proceedings of the fourteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 2011: 215−223.
[76]	Zang Y, Huang C, Loy C C. Fasa: Feature augmentation and sampling adaptation for long-tailed instance segmentation[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021: 3457−3466.
[77]	Park S, Hong Y, Heo B, et al. The majority can help the minority: Context-rich minority oversampling for long-tailed classification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 6887−6896.
[78]	Li B, Han Z, Li H, et al. Trustworthy long-tailed classification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 6970−6979.
[79]	Wang T, Zhu Y, Chen Y, et al. C2am loss: Chasing a better decision boundary for long-tail object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 6980−6989.
[80]	Buda M, Maki A, Mazurowski M A. A systematic study of the class imbalance problem in convolutional neural networks. Neural networks, 2018, 106: 249−259 doi: 10.1016/j.neunet.2018.07.011
[81]	Haixiang G, Yijing L, Shang J, et al. Learning from class-imbalanced data: Review of methods and applications. Expert systems with applications, 2017, 73: 220−239 doi: 10.1016/j.eswa.2016.12.035
[82]	Chawla N V, Bowyer K W, Hall L O, et al. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 2002, 16: 321−357 doi: 10.1613/jair.953
[83]	Jaiswal A, Babu A R, Zadeh M Z, et al. A survey on contrastive self-supervised learning. Technologies, 2020, 9(1): 2 doi: 10.3390/technologies9010002
[84]	Zhang H, Cisse M, Dauphin Y N, et al. mixup: Beyond empirical risk minimization[J]. arXiv preprint arXiv: 1710.09412, 2017.
[85]	Yun S, Han D, Oh S J, et al. Cutmix: Regularization strategy to train strong classifiers with localizable features[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2019: 6023−6032.
[86]	Verma V, Lamb A, Beckham C, et al. Manifold mixup: Better representations by interpolating hidden states[C]//International conference on machine learning. PMLR, 2019: 6438−6447.
[87]	Zhang S, Chen C, Zhang X, et al. Label-occurrence-balanced mixup for long-tailed recognition[C]//ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022: 3224−3228.
[88]	Zhang C, Pan T Y, Li Y, et al. Mosaicos: a simple and effective use of object-centric images for long-tailed object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021: 417−427.
[89]	Liu B, Li H, Kang H, et al. Breadcrumbs: Adversarial class-balanced sampling for long-tailed recognition[C]//European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022: 637−653.
[90]	Liu J, Li W, Sun Y. Memory-based jitter: Improving visual recognition on long-tailed data with diversity in memory[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2022, 36(2): 1720−1728.
[91]	Kingma D P, Welling M. An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 2019, 12(4): 307−392
[92]	Rangwani H, Jaswani N, Karmali T, et al. Improving GANs for Long-Tailed Data Through Group Spectral Regularization[C]//European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022: 426−442.
[93]	Rodriguez M G, Balduzzi D, Schölkopf B. Uncovering the temporal dynamics of diffusion networks[J]. arXiv preprint arXiv: 1105.0697, 2011.
[94]	Liu B, Li H, Kang H, et al. Semi-supervised long-tailed recognition using alternate sampling[J]. arXiv preprint arXiv: 2105.00133, 2021.
[95]	Wei C, Sohn K, Mellina C, et al. Crest: A class-rebalancing self-training framework for imbalanced semi-supervised learning[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 10857−10866.
[96]	Wang W, Zhao Z, Wang P, et al. Attentive feature augmentation for long-tailed visual recognition. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(9): 5803−5816 doi: 10.1109/TCSVT.2022.3161427
[97]	Wang Y, Pan X, Song S, et al. Implicit semantic data augmentation for deep networks. Advances in Neural Information Processing Systems, 201932
[98]	Li S, Gong K, Liu C H, et al. Metasaug: Meta semantic augmentation for long-tailed visual recognition[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 5212−5221.
[99]	Zhao Y, Chen W, Huang K, et al. Feature Re-Balancing for Long-Tailed Visual Recognition[C]//2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022: 1−8.
[100]	Vigneswaran R, Law M T, Balasubramanian V N, et al. Feature generation for long-tail classification[C]//Proceedings of the twelfth Indian conference on computer vision, graphics and image processing. 2021: 1−9.
[101]	Liu J, Sun Y, Han C, et al. Deep representation learning on long-tailed data: A learnable embedding augmentation perspective[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 2970−2979.
[102]	LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11): 2278−2324 doi: 10.1109/5.726791
[103]	He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770−778.
[104]	Xie S, Girshick R, Dollár P, et al. Aggregated residual transformations for deep neural networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1492−1500.
[105]	He K, Gkioxari G, Dollár P, et al. Mask r-cnn[C]//Proceedings of the IEEE international conference on computer vision. 2017: 2961−2969.
[106]	Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 201528
[107]	Long A, Yin W, Ajanthan T, et al. Retrieval augmented classification for long-tail visual recognition[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022: 6959−6969.
[108]	Zhou J, Li J, Yan Y, et al. Mixing Global and Local Features for Long-Tailed Expression Recognition. Information, 2023, 14(2): 83 doi: 10.3390/info14020083
[109]	Zhao W, Su Y, Hu M, et al. Hybrid ResNet based on joint basic and attention modules for long-tailed classification. International Journal of Approximate Reasoning, 2022, 150: 83−97 doi: 10.1016/j.ijar.2022.08.007
[110]	Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Advances in neural information processing systems, 201730
[111]	Chen J, Agarwal A, Abdelkarim S, et al. Reltransformer: A transformer-based long-tail visual relationship recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 19507−19517.
[112]	Hou Z, Yu B, Tao D. Batchformer: Learning to explore sample relationships for robust representation learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 7256−7266.
[113]	Ye H J, Chen H Y, Zhan D C, et al. Identifying and compensating for feature deviation in imbalanced deep learning[J]. arXiv preprint arXiv: 2001.01385, 2020.
[114]	Djouadi A, Bouktache E. A fast algorithm for the nearest-neighbor classifier. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(3): 277−282 doi: 10.1109/34.584107
[115]	Wei X S, Xu S L, Chen H, et al. Prototype-based classifier learning for long-tailed visual recognition. Science China Information Sciences, 2022, 65(6): 160105 doi: 10.1007/s11432-021-3489-1
[116]	Parisot S, Esperança P M, McDonagh S, et al. Long-tail recognition via compositional knowledge transfer[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 6939−6948.
[117]	Wu T Y, Morgado P, Wang P, et al. Solving long-tailed recognition with deep realistic taxonomic classifier[C]//Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VIII 16. Springer International Publishing, 2020: 171−189.
[118]	Jia Y, Peng X, Wang R, et al. Long-tailed Partial Label Learning by Head Classifier and Tail Classifier Cooperation[J]. 2024.
[119]	Duggal R, Freitas S, Dhamnani S, et al. Elf: An early-exiting framework for long-tailed classification[J]. arXiv preprint arXiv: 2006.11979, 2020.
[120]	Menon A K, Jayasumana S, Rawat A S, et al. Long-tail learning via logit adjustment[J]. arXiv preprint arXiv: 2007.07314, 2020.
[121]	Wang Y, Zhang B, Hou W, et al. Margin calibration for long-tailed visual recognition[C]//Asian Conference on Machine Learning. PMLR, 2023: 1101−1116.
[122]	Li M, Cheung Y, Lu Y. Long-tailed visual recognition via gaussian clouded logit adjustment[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 6929−6938.
[123]	Hong Y, Han S, Choi K, et al. Disentangling label distribution for long-tailed visual recognition[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 6626−6636.
[124]	Xu Z, Yang S, Wang X, et al. Rethink Long-Tailed Recognition with Vision Transforms[C]//ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023: 1−5.
[125]	He Y Y, Zhang P, Wei X S, et al. Relieving long-tailed instance segmentation via pairwise class balance[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 7000−7009.
[126]	Long H, Zhang X, Liu Y, et al. Mutual Exclusive Modulator for Long-Tailed Recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023: 4890−4899.
[127]	Huang C, Li Y, Loy C C, et al. Learning deep representation for imbalanced classification[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 5375−5384.
[128]	Jamal M A, Brown M, Yang M H, et al. Rethinking class-balanced methods for long-tailed visual recognition from a domain adaptation perspective[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 7610−7619.
[129]	Hsieh T I, Robb E, Chen H T, et al. Droploss for long-tail instance segmentation[C]//Proceedings of the AAAI conference on artificial intelligence. 2021, 35(2): 1549−1557.
[130]	Park S, Lim J, Jeon Y, et al. Influence-balanced loss for imbalanced visual classification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021: 735−744.
[131]	Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE international conference on computer vision. 2017: 2980−2988.
[132]	Smith L N. Cyclical focal loss[J]. arXiv preprint arXiv: 2202.08978, 2022.
[133]	Li B, Yao Y, Tan J, et al. Equalized focal loss for dense long-tailed object detection[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022: 6990−6999.
[134]	Wang T, Li Y, Kang B, et al. The devil is in classification: A simple framework for long-tail instance segmentation[C]//Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIV 16. Springer International Publishing, 2020: 728−744.
[135]	Li Y, Wang T, Kang B, et al. Overcoming classifier imbalance for long-tail object detection with balanced group softmax[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 10991−11000.
[136]	Zhong Z, Cui J, Liu S, et al. Improving calibration for long-tailed recognition[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 16489−16498.
[137]	Fan S, Zhang X, Song Z, et al. Cumulative dual-branch network framework for long-tailed multi-class classification. Engineering Applications of Artificial Intelligence, 2022, 114: 105080 doi: 10.1016/j.engappai.2022.105080
[138]	Guo H, Wang S. Long-tailed multi-label visual recognition by collaborative training on uniform and re-balanced samplings[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 15089−15098.
[139]	Bengio Y, Lamblin P, Popovici D, et al. Greedy layer-wise training of deep networks. Advances in neural information processing systems, 200619
[140]	Alshammari S, Wang Y X, Ramanan D, et al. Long-tailed recognition via weight balancing[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 6897−6907.
[141]	Zhu Z, Xing H, Xu Y. Easy balanced mixing for long-tailed data. Knowledge-Based Systems, 2022, 248: 108816 doi: 10.1016/j.knosys.2022.108816
[142]	Yang Y, Xu Z. Rethinking the value of labels for improving class-imbalanced learning. Advances in neural information processing systems, 2020, 33: 19290−19301
[143]	Liu X, Hu Y S, Cao X S, et al. Long-tailed class incremental learning[C]//European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022: 495−512.
[144]	Deng J, Dong W, Socher R, et al. Imagenet: A large-scale hierarchical image database[C]//2009 IEEE conference on computer vision and pattern recognition. Ieee, 2009: 248−255.
[145]	Wang Y, Gan W, Yang J, et al. Dynamic curriculum learning for imbalanced data classification[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2019: 5017−5026.
[146]	Wei T, Shi J X, Tu W W, et al. Robust long-tailed learning under label noise[J]. arXiv preprint arXiv: 2108.11569, 2021.
[147]	Li T, Wang L, Wu G. Self supervision to distillation for long-tailed visual recognition[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2021: 630−639.
[148]	Chen T, Kornblith S, Norouzi M, et al. A simple framework for contrastive learning of visual representations[C]//International conference on machine learning. PMLR, 2020: 1597−1607.
[149]	Wang P, Han K, Wei X S, et al. Contrastive learning based hybrid networks for long-tailed image classification[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 943−952.
[150]	Fu S, Chu H, He X, et al. Meta-prototype Decoupled Training for Long-Tailed Learning[C]//Proceedings of the Asian Conference on Computer Vision. 2022: 569−585.
[151]	Zhong Z, Cui J, Li Z, et al. Rebalanced Siamese Contrastive Mining for Long-Tailed Recognition[J]. arXiv preprint arXiv: 2203.11506, 2022.
[152]	Zhu J, Wang Z, Chen J, et al. Balanced contrastive learning for long-tailed visual recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 6908−6917.
[153]	Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network[J]. arXiv preprint arXiv: 1503.02531, 2015.
[154]	Iscen A, Araujo A, Gong B, et al. Class-balanced distillation for long-tailed visual recognition[J]. arXiv preprint arXiv: 2104.05279, 2021.
[155]	He Y Y, Wu J, Wei X S. Distilling virtual examples for long-tailed recognition[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021: 235−244.
[156]	Xia Y, Zhang S, Wang J, et al. One‐stage self‐distillation guided knowledge transfer for long‐tailed visual recognition. International Journal of Intelligent Systems, 2022, 37(12): 11893−11908 doi: 10.1002/int.23068
[157]	Yang C Y, Hsu H M, Cai J, et al. Long-tailed recognition of sar aerial view objects by cascading and paralleling experts[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 142−148.
[158]	Cui J, Liu S, Tian Z, et al. Reslt: Residual learning for long-tailed recognition. IEEE transactions on pattern analysis and machine intelligence, 2022, 45(3): 3695−3706
[159]	Wang X, Lian L, Miao Z, et al. Long-tailed recognition by routing diverse distribution-aware experts[J]. arXiv preprint arXiv: 2010.01809, 2020.
[160]	Li J, Tan Z, Wan J, et al. Nested collaborative learning for long-tailed visual recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 6949−6958.
[161]	Zhang Y, Hooi B, Hong L, et al. Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition. Advances in Neural Information Processing Systems, 2022, 35: 34077−34090
[162]	Chen Q, Liu Q, Lin E. A knowledge-guide hierarchical learning method for long-tailed image classification. Neurocomputing, 2021, 459: 408−418 doi: 10.1016/j.neucom.2021.07.008
[163]	Li Z, Zhao H, Lin Y. Multi-task convolutional neural network with coarse-to-fine knowledge transfer for long-tailed classification. Information Sciences, 2022, 608: 900−916 doi: 10.1016/j.ins.2022.07.015
[164]	Wen Y, Zhang K, Li Z, et al. A discriminative feature learning approach for deep face recognition[C]//Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part VII 14. Springer International Publishing, 2016: 499−515.
[165]	Cao D, Zhu X, Huang X, et al. Domain balancing: Face recognition on long-tailed domains[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 5671−5679.
[166]	Ma Y, Jiao L, Liu F, et al. Delving into Semantic Scale Imbalance[J]. arXiv preprint arXiv: 2212.14613, 2022.
[167]	Park B, Kim J, Cho S, et al. Balancing Domain Experts for Long-Tailed Camera-Trap Recognition[J]. arXiv preprint arXiv: 2202.07215, 2022.
[168]	Wang W, Wang M, Wang S, et al. One-shot learning for long-tail visual relation detection[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(07): 12225−12232.
[169]	Chang N, Yu Z, Wang Y X, et al. Image-level or object-level? a tale of two resampling strategies for long-tailed detection[C]//International conference on machine learning. PMLR, 2021: 1463−1472.
[170]	Zhang C, Lin G, Lai L, et al. Calibrating Class Activation Maps for Long-Tailed Visual Recognition[J]. arXiv preprint arXiv: 2108.12757, 2021.
[171]	Cao Y, Kuang J, Gao M, et al. Learning relation prototype from unlabeled texts for long-tail relation extraction. IEEE Transactions on Knowledge and Data Engineering, 2021
[172]	Zhang G, Liang R, Yu Z, et al. Rumour detection on social media with long-tail strategy[C]//2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022: 1−8.
[173]	Mottaghi A, Sarma P K, Amatriain X, et al. Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions[J]. arXiv preprint arXiv: 2011.06874, 2020.
[174]	Shi C, Hu B, Zhao W X, et al. Heterogeneous information network embedding for recommendation. IEEE Transactions on Knowledge and Data Engineering, 2018, 31(2): 357−370
[175]	Zhao T, Zhang X, Wang S. Graphsmote: Imbalanced node classification on graphs with graph neural networks[C]//Proceedings of the 14th ACM international conference on web search and data mining. 2021: 833−841.
[176]	Park J, Song J, Yang E. Graphens: Neighbor-aware ego network synthesis for class-imbalanced node classification[C]//International Conference on Learning Representations. 2021.
[177]	Yun S, Kim K, Yoon K, et al. Lte4g: long-tail experts for graph neural networks[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022: 2434−2443.
[178]	Hu Z, Dong Y, Wang K, et al. Gpt-gnn: Generative pre-training of graph neural networks[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020: 1857−1867.
[179]	Liu Z, Nguyen T K, Fang Y. Tail-gnn: Tail-node graph neural networks[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2021: 1109−1119.
[180]	Perrett T, Sinha S, Burghardt T, et al. Use Your Head: Improving Long-Tail Video Recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023: 2415−2425.
[181]	Tian C, Wang W, Zhu X, et al. Vl-ltr: Learning class-wise visual-linguistic representation for long-tailed visual recognition[C]//European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022: 73−91.
[182]	Ma T, Geng S, Wang M, et al. A simple long-tailed recognition baseline via vision-language model[J]. arXiv preprint arXiv: 2111.14745, 2021.
[183]	Wang R, Yu G, Domeniconi C, et al. Meta Cross-Modal Hashing on Long-Tailed Data[J]. arXiv preprint arXiv: 2111.04086, 2021.
[184]	Wang P, Wang X, Wang B, et al. Long-Tailed Time Series Classification via Feature Space Rebalancing[C]//International Conference on Database Systems for Advanced Applications. Cham: Springer Nature Switzerland, 2023: 151−166.
[185]	Deng J, Chen X, Jiang R, et al. St-norm: Spatial and temporal normalization for multi-variate time series forecasting[C]//Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. 2021: 269−278.
[186]	Craw S, Horsburgh B, Massie S. Music recommendation: audio neighbourhoods to discover music in the long tail[C]//Case-Based Reasoning Research and Development: 23rd International Conference, ICCBR 2015, Frankfurt am Main, Germany, September 28-30, 2015. Proceedings 23. Springer International Publishing, 2015: 73−87.
[187]	Deng K, Cheng G, Yang R, et al. Alleviating asr long-tailed problem by decoupling the learning of representation and classification. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021, 30: 340−354
[188]	Winata G I, Wang G, Xiong C, et al. Adapt-and-adjust: Overcoming the long-tail problem of multilingual speech recognition[J]. arXiv preprint arXiv: 2012.01687, 2020.
[189]	Peng P, Lu J, Tao S, et al. Progressively balanced supervised contrastive representation learning for long-tailed fault diagnosis. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1−12
[190]	Deng S, Lei Z, Liu J, et al. A Cost-Sensitive Dense Network for Fault Diagnosis under Data Imbalance[C]//2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD). IEEE, 2022: 1−6.
[191]	Jiao W, Zhang J. Sonar images classification while facing long-tail and few-shot. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1−20
[192]	Shao J, Zhu K, Zhang H, et al. DiffuLT: How to Make Diffusion Model Useful for Long-tail Recognition[J]. arXiv preprint arXiv: 2403.05170, 2024.
[193]	Shi J X, Wei T, Zhou Z, et al. Parameter-Efficient Long-Tailed Recognition[J]. arXiv preprint arXiv: 2309.10019, 2023.
[194]	Kabir H M. Reduction of Class Activation Uncertainty with Background Information[J]. arXiv preprint arXiv: 2305.03238, 2023.
[195]	Du F, Yang P, Jia Q, et al. Global and Local Mixture Consistency Cumulative Learning for Long-tailed Visual Recognitions[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023: 15814−15823.
[196]	Chen X, Liang C, Huang D, et al. Symbolic discovery of optimization algorithms[J]. arXiv preprint arXiv: 2302.06675, 2023.
[197]	Cui J, Zhong Z, Tian Z, et al. Generalized parametric contrastive learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
[198]	Hendrycks D, Gimpel K. A baseline for detecting misclassified and out-of-distribution examples in neural networks[J]. arXiv preprint arXiv: 1610.02136, 2016.
[199]	Liu W, Wang X, Owens J, et al. Energy-based out-of-distribution detection. Advances in neural information processing systems, 2020, 33: 21464−21475
[200]	Yang Y, Wang H, Katabi D. On multi-domain long-tailed recognition, imbalanced domain generalization and beyond[C]//European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022: 57−75.
[201]	Kim C D, Jeong J, Kim G. Imbalanced continual learning with partitioning reservoir sampling[C]//Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIII 16. Springer International Publishing, 2020: 411−428.
[202]	Ditzler G, Polikar R, Chawla N. An incremental learning algorithm for non-stationary environments and class imbalance[C]//2010 20th International Conference on Pattern Recognition. IEEE, 2010: 2997−3000.
[203]	Shi J X, Wei T, Li Y F. Residual diverse ensemble for long-tailed multi-label text classification. Science CHINA Information Science, 2024
[204]	Kharbanda S, Gupta D, Schultheis E, et al. Learning label-label correlations in Extreme Multi-label Classification via Label Features[J]. arXiv preprint arXiv: 2405.04545, 2024.
[205]	Zhang Y, Cao S, Mi S, et al. Learning sample representativeness for class-imbalanced multi-label classification. Pattern Analysis and Applications, 20241−12
[206]	Du C, Han Y, Huang G. SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning[J]. arXiv preprint arXiv: 2402.13505, 2024.
[207]	Ma C, Elezi I, Deng J, et al. Three heads are better than one: Complementary experts for long-tailed semi-supervised learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2024, 38(13): 14229−14237.
[208]	Shang X, Lu Y, Huang G, et al. Federated learning on heterogeneous and long-tailed data via classifier re-training with federated features[J]. arXiv preprint arXiv: 2204.13399, 2022.
[209]	Kou X, Xu C, Yang X, et al. Attention-guided Contrastive Hashing for Long-tailed Image Retrieval[C]//IJCAI. 2022: 1017−1023.
[210]	Geifman Y, El-Yaniv R. Deep active learning over the long tail[J]. arXiv preprint arXiv: 1711.00941, 2017.