{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:08:27Z","timestamp":1750219707428,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T00:00:00Z","timestamp":1701820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62266004"],"award-info":[{"award-number":["62266004"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,12,6]]},"DOI":"10.1145\/3595916.3626433","type":"proceedings-article","created":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T16:34:41Z","timestamp":1704126881000},"page":"1-7","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Representation Alignment and Uniformity in Long-tailed Classification"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5227-8884","authenticated-orcid":false,"given":"Yi","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Computer and Electronic Information, Guangxi University, CN"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4911-6958","authenticated-orcid":false,"given":"Zuqiang","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Computer and Electronic Information, Guangxi University, CN"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,1]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 6897\u20136907","author":"Alshammari Shaden","year":"2022","unstructured":"Shaden Alshammari , Yu-Xiong Wang , Deva Ramanan , and Shu Kong . 2022 . Long-tailed recognition via weight balancing . In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 6897\u20136907 . Shaden Alshammari, Yu-Xiong Wang, Deva Ramanan, and Shu Kong. 2022. Long-tailed recognition via weight balancing. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 6897\u20136907."},{"key":"e_1_3_2_1_2_1","volume-title":"Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18\u201322, 2017, Proceedings, Part I 10","author":"Ando Shin","year":"2017","unstructured":"Shin Ando and Chun\u00a0Yuan Huang . 2017 . Deep over-sampling framework for classifying imbalanced data . In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18\u201322, 2017, Proceedings, Part I 10 . Springer, 770\u2013785. Shin Ando and Chun\u00a0Yuan Huang. 2017. Deep over-sampling framework for classifying imbalanced data. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18\u201322, 2017, Proceedings, Part I 10. Springer, 770\u2013785."},{"key":"e_1_3_2_1_3_1","volume-title":"Learning representations by maximizing mutual information across views. Advances in neural information processing systems 32","author":"Bachman Philip","year":"2019","unstructured":"Philip Bachman , R\u00a0Devon Hjelm , and William Buchwalter . 2019. Learning representations by maximizing mutual information across views. Advances in neural information processing systems 32 ( 2019 ). Philip Bachman, R\u00a0Devon Hjelm, and William Buchwalter. 2019. Learning representations by maximizing mutual information across views. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_1_4_1","volume-title":"Learning imbalanced datasets with label-distribution-aware margin loss. Advances in neural information processing systems 32","author":"Cao Kaidi","year":"2019","unstructured":"Kaidi Cao , Colin Wei , Adrien Gaidon , Nikos Arechiga , and Tengyu Ma. 2019. Learning imbalanced datasets with label-distribution-aware margin loss. Advances in neural information processing systems 32 ( 2019 ). Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma. 2019. Learning imbalanced datasets with label-distribution-aware margin loss. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_1_5_1","volume-title":"International conference on machine learning. PMLR, 1597\u20131607","author":"Chen Ting","year":"2020","unstructured":"Ting Chen , Simon Kornblith , Mohammad Norouzi , and Geoffrey Hinton . 2020 . A simple framework for contrastive learning of visual representations . In International conference on machine learning. PMLR, 1597\u20131607 . Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597\u20131607."},{"key":"e_1_3_2_1_6_1","volume-title":"Proceedings, Part XXIX 16","author":"Chu Peng","year":"2020","unstructured":"Peng Chu , Xiao Bian , Shaopeng Liu , and Haibin Ling . 2020 . Feature space augmentation for long-tailed data. In Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020 , Proceedings, Part XXIX 16 . Springer, 694\u2013710. Peng Chu, Xiao Bian, Shaopeng Liu, and Haibin Ling. 2020. Feature space augmentation for long-tailed data. In Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXIX 16. Springer, 694\u2013710."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00020"},{"key":"e_1_3_2_1_8_1","volume-title":"Reslt: Residual learning for long-tailed recognition","author":"Cui Jiequan","year":"2022","unstructured":"Jiequan Cui , Shu Liu , Zhuotao Tian , Zhisheng Zhong , and Jiaya Jia . 2022 . Reslt: Residual learning for long-tailed recognition . IEEE transactions on pattern analysis and machine intelligence 45, 3 (2022), 3695\u20133706. Jiequan Cui, Shu Liu, Zhuotao Tian, Zhisheng Zhong, and Jiaya Jia. 2022. Reslt: Residual learning for long-tailed recognition. IEEE transactions on pattern analysis and machine intelligence 45, 3 (2022), 3695\u20133706."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00075"},{"key":"e_1_3_2_1_10_1","volume-title":"Generalized parametric contrastive learning","author":"Cui Jiequan","year":"2023","unstructured":"Jiequan Cui , Zhisheng Zhong , Zhuotao Tian , Shu Liu , Bei Yu , and Jiaya Jia . 2023. Generalized parametric contrastive learning . IEEE Transactions on Pattern Analysis and Machine Intelligence ( 2023 ). Jiequan Cui, Zhisheng Zhong, Zhuotao Tian, Shu Liu, Bei Yu, and Jiaya Jia. 2023. Generalized parametric contrastive learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00949"},{"key":"e_1_3_2_1_12_1","volume-title":"Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552","author":"DeVries Terrance","year":"2017","unstructured":"Terrance DeVries and Graham\u00a0 W Taylor . 2017. Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 ( 2017 ). Terrance DeVries and Graham\u00a0W Taylor. 2017. Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)."},{"key":"e_1_3_2_1_13_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 15814\u201315823","author":"Du Fei","year":"2023","unstructured":"Fei Du , Peng Yang , Qi Jia , Fengtao Nan , Xiaoting Chen , and Yun Yang . 2023 . Global and Local Mixture Consistency Cumulative Learning for Long-tailed Visual Recognitions . In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 15814\u201315823 . Fei Du, Peng Yang, Qi Jia, Fengtao Nan, Xiaoting Chen, and Yun Yang. 2023. Global and Local Mixture Consistency Cumulative Learning for Long-tailed Visual Recognitions. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 15814\u201315823."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00656"},{"key":"e_1_3_2_1_16_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 7610\u20137619","author":"Jamal Muhammad\u00a0Abdullah","year":"2020","unstructured":"Muhammad\u00a0Abdullah Jamal , Matthew Brown , Ming-Hsuan Yang , Liqiang Wang , and Boqing Gong . 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. 7610\u20137619 . Muhammad\u00a0Abdullah Jamal, Matthew Brown, Ming-Hsuan Yang, Liqiang Wang, and Boqing Gong. 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. 7610\u20137619."},{"key":"e_1_3_2_1_17_1","volume-title":"International Conference on Learning Representations.","author":"Kang Bingyi","year":"2020","unstructured":"Bingyi Kang , Yu Li , Sa Xie , Zehuan Yuan , and Jiashi Feng . 2020 . Exploring balanced feature spaces for representation learning . In International Conference on Learning Representations. Bingyi Kang, Yu Li, Sa Xie, Zehuan Yuan, and Jiashi Feng. 2020. Exploring balanced feature spaces for representation learning. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_18_1","volume-title":"Decoupling Representation and Classifier for Long-Tailed Recognition. In International Conference on Learning Representations.","author":"Kang Bingyi","year":"2019","unstructured":"Bingyi Kang , Saining Xie , Marcus Rohrbach , Zhicheng Yan , Albert Gordo , Jiashi Feng , and Yannis Kalantidis . 2019 . Decoupling Representation and Classifier for Long-Tailed Recognition. In International Conference on Learning Representations. Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis. 2019. Decoupling Representation and Classifier for Long-Tailed Recognition. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_19_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 6949\u20136958","author":"Li Jun","year":"2022","unstructured":"Jun Li , Zichang Tan , Jun Wan , Zhen Lei , and Guodong Guo . 2022 . Nested collaborative learning for long-tailed visual recognition . In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 6949\u20136958 . Jun Li, Zichang Tan, Jun Wan, Zhen Lei, and Guodong Guo. 2022. Nested collaborative learning for long-tailed visual recognition. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 6949\u20136958."},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 5212\u20135221","author":"Li Shuang","year":"2021","unstructured":"Shuang Li , Kaixiong Gong , Chi\u00a0Harold Liu , Yulin Wang , Feng Qiao , and Xinjing Cheng . 2021 . Metasaug: Meta semantic augmentation for long-tailed visual recognition . In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 5212\u20135221 . Shuang Li, Kaixiong Gong, Chi\u00a0Harold Liu, Yulin Wang, Feng Qiao, and Xinjing Cheng. 2021. Metasaug: Meta semantic augmentation for long-tailed visual recognition. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 5212\u20135221."},{"key":"e_1_3_2_1_21_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 6918\u20136928","author":"Li Tianhong","year":"2022","unstructured":"Tianhong Li , Peng Cao , Yuan Yuan , Lijie Fan , Yuzhe Yang , Rogerio\u00a0 S Feris , Piotr Indyk , and Dina Katabi . 2022 . Targeted supervised contrastive learning for long-tailed recognition . In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 6918\u20136928 . Tianhong Li, Peng Cao, Yuan Yuan, Lijie Fan, Yuzhe Yang, Rogerio\u00a0S Feris, Piotr Indyk, and Dina Katabi. 2022. Targeted supervised contrastive learning for long-tailed recognition. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 6918\u20136928."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"e_1_3_2_1_23_1","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2970\u20132979","author":"Liu Jialun","year":"2020","unstructured":"Jialun Liu , Yifan Sun , Chuchu Han , Zhaopeng Dou , and Wenhui Li . 2020 . Deep representation learning on long-tailed data: A learnable embedding augmentation perspective . In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2970\u20132979 . Jialun Liu, Yifan Sun, Chuchu Han, Zhaopeng Dou, and Wenhui Li. 2020. Deep representation learning on long-tailed data: A learnable embedding augmentation perspective. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2970\u20132979."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00264"},{"key":"e_1_3_2_1_25_1","volume-title":"International Conference on Learning Representations.","author":"Menon Aditya\u00a0Krishna","year":"2020","unstructured":"Aditya\u00a0Krishna Menon , Sadeep Jayasumana , Ankit\u00a0Singh Rawat , Himanshu Jain , Andreas Veit , and Sanjiv Kumar . 2020 . Long-tail learning via logit adjustment . In International Conference on Learning Representations. Aditya\u00a0Krishna Menon, Sadeep Jayasumana, Ankit\u00a0Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar. 2020. Long-tail learning via logit adjustment. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_26_1","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision. 735\u2013744","author":"Park Seulki","year":"2021","unstructured":"Seulki Park , Jongin Lim , Younghan Jeon , and Jin\u00a0Young Choi . 2021 . Influence-balanced loss for imbalanced visual classification . In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 735\u2013744 . Seulki Park, Jongin Lim, Younghan Jeon, and Jin\u00a0Young Choi. 2021. Influence-balanced loss for imbalanced visual classification. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 735\u2013744."},{"key":"e_1_3_2_1_27_1","volume-title":"Balanced meta-softmax for long-tailed visual recognition. Advances in neural information processing systems 33","author":"Ren Jiawei","year":"2020","unstructured":"Jiawei Ren , Cunjun Yu , Xiao Ma , Haiyu Zhao , Shuai Yi , 2020. Balanced meta-softmax for long-tailed visual recognition. Advances in neural information processing systems 33 ( 2020 ), 4175\u20134186. Jiawei Ren, Cunjun Yu, Xiao Ma, Haiyu Zhao, Shuai Yi, 2020. Balanced meta-softmax for long-tailed visual recognition. Advances in neural information processing systems 33 (2020), 4175\u20134186."},{"key":"e_1_3_2_1_28_1","volume-title":"International Conference on Machine Learning. PMLR, 8242\u20138252","author":"Roth Karsten","year":"2020","unstructured":"Karsten Roth , Timo Milbich , Samarth Sinha , Prateek Gupta , Bjorn Ommer , and Joseph\u00a0Paul Cohen . 2020 . Revisiting training strategies and generalization performance in deep metric learning . In International Conference on Machine Learning. PMLR, 8242\u20138252 . Karsten Roth, Timo Milbich, Samarth Sinha, Prateek Gupta, Bjorn Ommer, and Joseph\u00a0Paul Cohen. 2020. Revisiting training strategies and generalization performance in deep metric learning. In International Conference on Machine Learning. PMLR, 8242\u20138252."},{"key":"e_1_3_2_1_29_1","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision. 9495\u20139504","author":"Samuel Dvir","year":"2021","unstructured":"Dvir Samuel and Gal Chechik . 2021 . Distributional robustness loss for long-tail learning . In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 9495\u20139504 . Dvir Samuel and Gal Chechik. 2021. Distributional robustness loss for long-tail learning. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 9495\u20139504."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","first-page":"444","DOI":"10.23883\/IJRTER.2017.3168.0UWXM","article-title":"A review on imbalanced data handling using undersampling and oversampling technique","volume":"3","author":"Shelke S","year":"2017","unstructured":"Mayuri\u00a0 S Shelke , Prashant\u00a0 R Deshmukh , and Vijaya\u00a0 K Shandilya . 2017 . A review on imbalanced data handling using undersampling and oversampling technique . Int. J. Recent Trends Eng. Res 3 , 4 (2017), 444 \u2013 449 . Mayuri\u00a0S Shelke, Prashant\u00a0R Deshmukh, and Vijaya\u00a0K Shandilya. 2017. A review on imbalanced data handling using undersampling and oversampling technique. Int. J. Recent Trends Eng. Res 3, 4 (2017), 444\u2013449.","journal-title":"Int. J. Recent Trends Eng. Res"},{"key":"e_1_3_2_1_31_1","first-page":"1513","article-title":"Long-tailed classification by keeping the good and removing the bad momentum causal effect","volume":"33","author":"Tang Kaihua","year":"2020","unstructured":"Kaihua Tang , Jianqiang Huang , and Hanwang Zhang . 2020 . Long-tailed classification by keeping the good and removing the bad momentum causal effect . Advances in Neural Information Processing Systems 33 (2020), 1513 \u2013 1524 . Kaihua Tang, Jianqiang Huang, and Hanwang Zhang. 2020. Long-tailed classification by keeping the good and removing the bad momentum causal effect. Advances in Neural Information Processing Systems 33 (2020), 1513\u20131524.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_32_1","volume-title":"Proceedings, Part XI 16","author":"Tian Yonglong","year":"2020","unstructured":"Yonglong Tian , Dilip Krishnan , and Phillip Isola . 2020 . Contrastive multiview coding. In Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020 , Proceedings, Part XI 16 . Springer, 776\u2013794. Yonglong Tian, Dilip Krishnan, and Phillip Isola. 2020. Contrastive multiview coding. In Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XI 16. Springer, 776\u2013794."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00378"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00100"},{"key":"e_1_3_2_1_35_1","volume-title":"International Conference on Machine Learning. PMLR, 9929\u20139939","author":"Wang Tongzhou","year":"2020","unstructured":"Tongzhou Wang and Phillip Isola . 2020 . Understanding contrastive representation learning through alignment and uniformity on the hypersphere . In International Conference on Machine Learning. PMLR, 9929\u20139939 . Tongzhou Wang and Phillip Isola. 2020. Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In International Conference on Machine Learning. PMLR, 9929\u20139939."},{"key":"e_1_3_2_1_36_1","volume-title":"International Conference on Learning Representations.","author":"Wang Xudong","year":"2020","unstructured":"Xudong Wang , Long Lian , Zhongqi Miao , Ziwei Liu , and Stella Yu . 2020 . Long-tailed Recognition by Routing Diverse Distribution-Aware Experts . In International Conference on Learning Representations. Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, and Stella Yu. 2020. Long-tailed Recognition by Routing Diverse Distribution-Aware Experts. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_37_1","volume-title":"Proceedings, Part V 16","author":"Xiang Liuyu","year":"2020","unstructured":"Liuyu Xiang , Guiguang Ding , and Jungong Han . 2020 . Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification. In Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020 , Proceedings, Part V 16 . Springer, 247\u2013263. Liuyu Xiang, Guiguang Ding, and Jungong Han. 2020. Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification. In Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part V 16. Springer, 247\u2013263."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"e_1_3_2_1_39_1","volume-title":"Rethinking the value of labels for improving class-imbalanced learning. Advances in neural information processing systems 33","author":"Yang Yuzhe","year":"2020","unstructured":"Yuzhe Yang and Zhi Xu. 2020. Rethinking the value of labels for improving class-imbalanced learning. Advances in neural information processing systems 33 ( 2020 ), 19290\u201319301. Yuzhe Yang and Zhi Xu. 2020. Rethinking the value of labels for improving class-imbalanced learning. Advances in neural information processing systems 33 (2020), 19290\u201319301."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00585"},{"key":"e_1_3_2_1_41_1","volume-title":"mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412","author":"Zhang Hongyi","year":"2017","unstructured":"Hongyi Zhang , Moustapha Cisse , Yann\u00a0 N Dauphin , and David Lopez-Paz . 2017. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 ( 2017 ). Hongyi Zhang, Moustapha Cisse, Yann\u00a0N Dauphin, and David Lopez-Paz. 2017. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00239"},{"key":"e_1_3_2_1_43_1","volume-title":"Proceedings of the AAAI conference on artificial intelligence, Vol.\u00a035","author":"Zhang Yongshun","year":"2021","unstructured":"Yongshun Zhang , Xiu-Shen Wei , Boyan Zhou , and Jianxin Wu . 2021 . Bag of tricks for long-tailed visual recognition with deep convolutional neural networks . In Proceedings of the AAAI conference on artificial intelligence, Vol.\u00a035 . 3447\u20133455. Yongshun Zhang, Xiu-Shen Wei, Boyan Zhou, and Jianxin Wu. 2021. Bag of tricks for long-tailed visual recognition with deep convolutional neural networks. In Proceedings of the AAAI conference on artificial intelligence, Vol.\u00a035. 3447\u20133455."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00974"},{"key":"e_1_3_2_1_45_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 6908\u20136917","author":"Zhu Jianggang","year":"2022","unstructured":"Jianggang Zhu , Zheng Wang , Jingjing Chen , Yi- Ping\u00a0Phoebe Chen , and Yu-Gang Jiang . 2022 . Balanced contrastive learning for long-tailed visual recognition . In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 6908\u20136917 . Jianggang Zhu, Zheng Wang, Jingjing Chen, Yi-Ping\u00a0Phoebe Chen, and Yu-Gang Jiang. 2022. Balanced contrastive learning for long-tailed visual recognition. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 6908\u20136917."}],"event":{"name":"MMAsia '23: ACM Multimedia Asia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Tainan Taiwan","acronym":"MMAsia '23"},"container-title":["ACM Multimedia Asia 2023"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3595916.3626433","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3595916.3626433","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:35:56Z","timestamp":1750178156000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3595916.3626433"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,6]]},"references-count":45,"alternative-id":["10.1145\/3595916.3626433","10.1145\/3595916"],"URL":"https:\/\/doi.org\/10.1145\/3595916.3626433","relation":{},"subject":[],"published":{"date-parts":[[2023,12,6]]},"assertion":[{"value":"2024-01-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}