{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T18:18:05Z","timestamp":1776881885865,"version":"3.51.2"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,28]]},"DOI":"10.1145\/3664647.3681106","type":"proceedings-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T06:59:49Z","timestamp":1729925989000},"page":"2888-2897","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Prior-free Balanced Replay: Uncertainty-guided Reservoir Sampling for Long-Tailed Continual Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8109-5248","authenticated-orcid":false,"given":"Lei","family":"Liu","sequence":"first","affiliation":[{"name":"Shenzhen Research Institute of Big Data (SRIBD) & The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4497-0135","authenticated-orcid":false,"given":"Li","family":"Liu","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou) & The Hong Kong University of Science and Technology, Guangzhou, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9337-687X","authenticated-orcid":false,"given":"Yawen","family":"Cui","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Gradient based sample selection for online continual learning. Advances in Neural Information Processing systems","author":"Aljundi Rahaf","year":"1816","unstructured":"Rahaf Aljundi, Min Lin, Baptiste Goujaud, and Yoshua Bengio. 2019. Gradient based sample selection for online continual learning. Advances in Neural Information Processing systems, Vol. 32, 11816--11825."},{"key":"e_1_3_2_1_2_1","volume-title":"International Conference on Learning Representations.","author":"Benjamin Ari","year":"2018","unstructured":"Ari Benjamin, David Rolnick, and Konrad Kording. 2018. Measuring and regularizing networks in function space. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_3_1","volume-title":"Dark experience for general continual learning: a strong, simple baseline. Advances in Neural Information Processing systems","author":"Buzzega Pietro","year":"2020","unstructured":"Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, and Simone Calderara. 2020. Dark experience for general continual learning: a strong, simple baseline. Advances in Neural Information Processing systems, Vol. 33 (2020), 15920--15930."},{"key":"e_1_3_2_1_4_1","unstructured":"Kaidi Cao Colin Wei Adrien Gaidon Nikos Arechiga and Tengyu Ma. 2019. Learning imbalanced datasets with label-distribution-aware margin loss. In Advances in Neural Information Processing systems. 1567--1578."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01252-6_33"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16861"},{"key":"e_1_3_2_1_7_1","volume-title":"Efficient Lifelong Learning with A-GEM. In International Conference on Learning Representations.","author":"Chaudhry Arslan","year":"2018","unstructured":"Arslan Chaudhry, Marc-Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny. 2018. Efficient Lifelong Learning with A-GEM. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"e_1_3_2_1_9_1","volume-title":"International Conference on Machine Learning. PMLR","author":"Chrysakis Aristotelis","year":"2020","unstructured":"Aristotelis Chrysakis and Marie-Francine Moens. 2020. Online continual learning from imbalanced data. In International Conference on Machine Learning. PMLR, 1952--1961."},{"key":"e_1_3_2_1_10_1","volume-title":"International Conference on Machine Learning. 1050--1059","author":"Gal Yarin","year":"2016","unstructured":"Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In International Conference on Machine Learning. 1050--1059."},{"key":"e_1_3_2_1_11_1","volume-title":"Efficient architecture search for continual learning","author":"Gao Qiang","year":"2022","unstructured":"Qiang Gao, Zhipeng Luo, Diego Klabjan, and Fengli Zhang. 2022. Efficient architecture search for continual learning. IEEE Transactions on Neural Networks and Learning Systems (2022)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.740"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00395"},{"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","volume-title":"NeurIPS workshop","volume":"1050","author":"Hinton Geoffrey","year":"2015","unstructured":"Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the Knowledge in a Neural Network. NeurIPS workshop, Vol. 1050 (2015), 9."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00092"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3196092"},{"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."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58601-0_25"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01391"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2020.3045735"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2773081"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3219605"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"e_1_3_2_1_26_1","volume-title":"Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification. In International Conference on Tools with Artificial Intelligence. IEEE, 1268--1273","author":"Liu Lei","year":"2020","unstructured":"Lei Liu, Wentao Lei, Xiang Wan, Li Liu, Yongfang Luo, and Cheng Feng. 2020. Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification. In International Conference on Tools with Artificial Intelligence. IEEE, 1268--1273."},{"key":"e_1_3_2_1_27_1","volume-title":"Long-Tailed Class Incremental Learning. In European Conference on Computer Vision. Springer, 495--512","author":"Liu Xialei","year":"2022","unstructured":"Xialei Liu, Yu-Song Hu, Xu-Sheng Cao, Andrew D Bagdanov, Ke Li, and Ming-Ming Cheng. 2022. Long-Tailed Class Incremental Learning. In European Conference on Computer Vision. Springer, 495--512."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00264"},{"key":"e_1_3_2_1_29_1","volume-title":"Gradient episodic memory for continual learning. Advances in Neural Information Processing systems","author":"Lopez-Paz David","year":"2017","unstructured":"David Lopez-Paz and Marc'Aurelio Ranzato. 2017. Gradient episodic memory for continual learning. Advances in Neural Information Processing systems, Vol. 30 (2017), 6467--6476."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01418-6_38"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00810"},{"key":"e_1_3_2_1_32_1","volume-title":"International Conference on Learning Representations.","author":"Menon Aditya Krishna","year":"2020","unstructured":"Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh 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_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00128"},{"key":"e_1_3_2_1_34_1","volume-title":"International Conference on Machine Learning. PMLR, 25006--25017","author":"Mo Shentong","year":"2023","unstructured":"Shentong Mo and Pedro Morgado. 2023. A unified audio-visual learning framework for localization, separation, and recognition. In International Conference on Machine Learning. PMLR, 25006--25017."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00077"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-021-01614-0"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1037\/0033-295X.97.2.285"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.587"},{"key":"e_1_3_2_1_39_1","first-page":"4175","article-title":"Balanced meta-softmax for long-tailed visual recognition","volume":"33","author":"Ren Jiawei","year":"2020","unstructured":"Jiawei Ren, Cunjun Yu, Xiao Ma, Haiyu Zhao, Shuai Yi, et al. 2020. Balanced meta-softmax for long-tailed visual recognition. Advances in Neural Information Processing Systems, Vol. 33 (2020), 4175--4186.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_40_1","volume-title":"International Conference on Learning Representations. 4334--4343","author":"Ren Mengye","year":"2018","unstructured":"Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun. 2018. Learning to reweight examples for robust deep learning. In International Conference on Learning Representations. 4334--4343."},{"key":"e_1_3_2_1_41_1","volume-title":"International Conference on Learning Representations.","author":"Riemer Matthew","year":"2018","unstructured":"Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro. 2018. Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1080\/09540099550039318"},{"key":"e_1_3_2_1_43_1","volume-title":"Progressive neural networks. arXiv preprint arXiv:1606.04671","author":"Rusu Andrei A","year":"2016","unstructured":"Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell. 2016. Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016)."},{"key":"e_1_3_2_1_44_1","volume-title":"International Conference on Machine Learning. 4528--4537","author":"Schwarz Jonathan","year":"2018","unstructured":"Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell. 2018. Progress & compress: A scalable framework for continual learning. In International Conference on Machine Learning. 4528--4537."},{"key":"e_1_3_2_1_45_1","volume-title":"International Conference on Machine Learning. 4548--4557","author":"Serra Joan","year":"2018","unstructured":"Joan Serra, Didac Suris, Marius Miron, and Alexandros Karatzoglou. 2018. Overcoming catastrophic forgetting with hard attention to the task. In International Conference on Machine Learning. 4548--4557."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3150906"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3096814"},{"key":"e_1_3_2_1_48_1","unstructured":"Oriol Vinyals Charles Blundell Timothy Lillicrap Daan Wierstra et al. 2016. Matching networks for one shot learning. Advances in Neural Information Processing systems Vol. 29 (2016)."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3147.3165"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00024"},{"key":"e_1_3_2_1_51_1","volume-title":"International Conference on Learning Representations.","author":"Xie Cihang","year":"2019","unstructured":"Cihang Xie and Alan Yuille. 2019. Intriguing Properties of Adversarial Training at Scale. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_52_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 109--118","author":"Yan Qingsen","unstructured":"Qingsen Yan, Dong Gong, Yuhang Liu, Anton van den Hengel, and Javen Qinfeng Shi. 2022. Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 109--118."},{"key":"e_1_3_2_1_53_1","volume-title":"International Conference on Machine Learning. PMLR, 3987--3995","author":"Zenke Friedemann","year":"2017","unstructured":"Friedemann Zenke, Ben Poole, and Surya Ganguli. 2017. Continual learning through synaptic intelligence. In International Conference on Machine Learning. PMLR, 3987--3995."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19842-7_16"}],"event":{"name":"MM '24: The 32nd ACM International Conference on Multimedia","location":"Melbourne VIC Australia","acronym":"MM '24","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 32nd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3681106","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3664647.3681106","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:57:52Z","timestamp":1750294672000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3681106"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,28]]},"references-count":54,"alternative-id":["10.1145\/3664647.3681106","10.1145\/3664647"],"URL":"https:\/\/doi.org\/10.1145\/3664647.3681106","relation":{},"subject":[],"published":{"date-parts":[[2024,10,28]]},"assertion":[{"value":"2024-10-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}