{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T15:56:05Z","timestamp":1783526165459,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":40,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819609109","type":"print"},{"value":"9789819609116","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T00:00:00Z","timestamp":1733616000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T00:00:00Z","timestamp":1733616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-0911-6_20","type":"book-chapter","created":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T08:00:55Z","timestamp":1733558455000},"page":"335-351","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["TaE: Task-Aware Expandable Representation for\u00a0Long Tail Class Incremental Learning"],"prefix":"10.1007","author":[{"given":"Linjie","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenyu","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaming","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,8]]},"reference":[{"key":"20_CR1","unstructured":"Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. Advances in neural information processing systems 32 (2019)"},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Chu, P., Bian, X., Liu, S., Ling, H.: 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. pp. 694\u2013710. Springer (2020)","DOI":"10.1007\/978-3-030-58526-6_41"},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Douillard, A., Cord, M., Ollion, C., Robert, T., Valle, E.: Podnet: Pooled outputs distillation for small-tasks incremental learning. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XX 16. pp. 86\u2013102. Springer (2020)","DOI":"10.1007\/978-3-030-58565-5_6"},{"issue":"4","key":"20_CR4","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/S1364-6613(99)01294-2","volume":"3","author":"RM French","year":"1999","unstructured":"French, R.M.: Catastrophic forgetting in connectionist networks. Trends Cogn. Sci. 3(4), 128\u2013135 (1999)","journal-title":"Trends Cogn. Sci."},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"He, H., Cai, J., Zhang, J., Tao, D., Zhuang, B.: Sensitivity-aware visual parameter-efficient tuning. arXiv preprint arXiv:2303.08566 (2023)","DOI":"10.1109\/ICCV51070.2023.01086"},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part IV 14. pp. 630\u2013645. Springer (2016)","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 831\u2013839 (2019)","DOI":"10.1109\/CVPR.2019.00092"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Kalla, J., Biswas, S.: Robust feature learning and global variance-driven classifier alignment for long-tail class incremental learning. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. pp. 32\u201341 (2024)","DOI":"10.1109\/WACV57701.2024.00011"},{"key":"20_CR9","unstructured":"Kang, B., Xie, S., Rohrbach, M., Yan, Z., Gordo, A., Feng, J., Kalantidis, Y.: Decoupling representation and classifier for long-tailed recognition. arXiv preprint arXiv:1910.09217 (2019)"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Kim, D., Han, B.: On the stability-plasticity dilemma of class-incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 20196\u201320204 (2023)","DOI":"10.1109\/CVPR52729.2023.01934"},{"issue":"13","key":"20_CR11","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1073\/pnas.1611835114","volume":"114","author":"J Kirkpatrick","year":"2017","unstructured":"Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proc. Natl. Acad. Sci. 114(13), 3521\u20133526 (2017)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"20_CR12","unstructured":"Krizhevsky, A., Hinton, G., et\u00a0al.: Learning multiple layers of features from tiny images (2009)"},{"issue":"12","key":"20_CR13","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","volume":"40","author":"Z Li","year":"2017","unstructured":"Li, Z., Hoiem, D.: Learning without forgetting. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 2935\u20132947 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Liu, B., Li, H., Kang, H., Hua, G., Vasconcelos, N.: Gistnet: a geometric structure transfer network for long-tailed recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 8209\u20138218 (2021)","DOI":"10.1109\/ICCV48922.2021.00810"},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Liu, X., Hu, Y.S., Cao, X.S., Bagdanov, A.D., Li, K., Cheng, M.M.: Long-tailed class incremental learning. In: European Conference on Computer Vision. pp. 495\u2013512. Springer (2022)","DOI":"10.1007\/978-3-031-19827-4_29"},{"key":"20_CR17","unstructured":"Martinetz, T., Schulten, K., et\u00a0al.: A\" neural-gas\" network learns topologies (1991)"},{"key":"20_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106460","volume":"208","author":"D Mu\u00f1oz","year":"2020","unstructured":"Mu\u00f1oz, D., Narv\u00e1ez, C., Cobos, C., Mendoza, M., Herrera, F.: Incremental learning model inspired in rehearsal for deep convolutional networks. Knowl.-Based Syst. 208, 106460 (2020)","journal-title":"Knowl.-Based Syst."},{"key":"20_CR19","unstructured":"Muzammal, N., Kanchana, R., Khan\u00a0Salman, H., Munawar, H., Fahad, S.K., Ming-Hsuan, Y.: Intriguing properties of vision transformers. arXiv preprint arXiv:2105.104973 (2021)"},{"key":"20_CR20","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: Automatic differentiation in pytorch (2017)"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Prudent, Y., Ennaji, A.: An incremental growing neural gas learns topologies. In: Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. vol.\u00a02, pp. 1211\u20131216. IEEE (2005)","DOI":"10.1109\/IJCNN.2005.1556026"},{"key":"20_CR22","first-page":"12116","volume":"34","author":"M Raghu","year":"2021","unstructured":"Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C., Dosovitskiy, A.: Do vision transformers see like convolutional neural networks? Adv. Neural. Inf. Process. Syst. 34, 12116\u201312128 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"20_CR23","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. pp. 2001\u20132010 (2017)","DOI":"10.1109\/CVPR.2017.587"},{"key":"20_CR24","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115, 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"20_CR25","doi-asserted-by":"crossref","unstructured":"Tao, X., Hong, X., Chang, X., Dong, S., Wei, X., Gong, Y.: Few-shot class-incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 12183\u201312192 (2020)","DOI":"10.1109\/CVPR42600.2020.01220"},{"key":"20_CR26","unstructured":"Wang, F.Y., Zhou, D.W., Liu, L., Ye, H.J., Bian, Y., Zhan, D.C., Zhao, P.: Beef: Bi-compatible class-incremental learning via energy-based expansion and fusion. In: The Eleventh International Conference on Learning Representations (2022)"},{"key":"20_CR27","doi-asserted-by":"crossref","unstructured":"Wang, F.Y., Zhou, D.W., Ye, H.J., Zhan, D.C.: Foster: Feature boosting and compression for class-incremental learning. In: European conference on computer vision. pp. 398\u2013414. Springer (2022)","DOI":"10.1007\/978-3-031-19806-9_23"},{"key":"20_CR28","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhang, X., Su, H., Zhu, J.: A comprehensive survey of continual learning: Theory, method and application. IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)","DOI":"10.1109\/TPAMI.2024.3367329"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Wang, S., Shi, W., Dong, S., Gao, X., Song, X., Gong, Y.: Semantic knowledge guided class-incremental learning. IEEE Transactions on Circuits and Systems for Video Technology (2023)","DOI":"10.1109\/TCSVT.2023.3262739"},{"key":"20_CR30","doi-asserted-by":"crossref","unstructured":"Wang, X., Yang, X., Yin, J., Wei, K., Deng, C.: Long-tail class incremental learning via independent sub-prototype construction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 28598\u201328607 (2024)","DOI":"10.1109\/CVPR52733.2024.02702"},{"key":"20_CR31","unstructured":"Wang, Y.X., Ramanan, D., Hebert, M.: Learning to model the tail. Advances in neural information processing systems 30 (2017)"},{"key":"20_CR32","doi-asserted-by":"crossref","unstructured":"Welling, M.: Herding dynamical weights to learn. In: Proceedings of the 26th Annual International Conference on Machine Learning. pp. 1121\u20131128 (2009)","DOI":"10.1145\/1553374.1553517"},{"key":"20_CR33","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.ins.2015.02.024","volume":"307","author":"P Xia","year":"2015","unstructured":"Xia, P., Zhang, L., Li, F.: Learning similarity with cosine similarity ensemble. Inf. Sci. 307, 39\u201352 (2015)","journal-title":"Inf. Sci."},{"key":"20_CR34","doi-asserted-by":"crossref","unstructured":"Xiang, L., Ding, G., Han, J.: 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. pp. 247\u2013263. Springer (2020)","DOI":"10.1007\/978-3-030-58558-7_15"},{"key":"20_CR35","doi-asserted-by":"crossref","unstructured":"Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 3014\u20133023 (2021)","DOI":"10.1109\/CVPR46437.2021.00303"},{"key":"20_CR36","doi-asserted-by":"crossref","unstructured":"Yin, X., Yu, X., Sohn, K., Liu, X., Chandraker, M.: Feature transfer learning for face recognition with under-represented data. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 5704\u20135713 (2019)","DOI":"10.1109\/CVPR.2019.00585"},{"key":"20_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Kang, B., Hooi, B., Yan, S., Feng, J.: Deep long-tailed learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)","DOI":"10.1109\/TPAMI.2023.3268118"},{"key":"20_CR38","doi-asserted-by":"crossref","unstructured":"Zhou, D.W., Wang, F.Y., Ye, H.J., Zhan, D.C.: Pycil: A python toolbox for class-incremental learning (2023)","DOI":"10.1007\/s11432-022-3600-y"},{"key":"20_CR39","unstructured":"Zhou, D.W., Wang, Q.W., Qi, Z.H., Ye, H.J., Zhan, D.C., Liu, Z.: Deep class-incremental learning: A survey. arXiv preprint arXiv:2302.03648 (2023)"},{"key":"20_CR40","unstructured":"Zhou, D.W., Wang, Q.W., Ye, H.J., Zhan, D.C.: A model or 603 exemplars: Towards memory-efficient class-incremental learning. arXiv preprint arXiv:2205.13218 (2022)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0911-6_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T08:20:55Z","timestamp":1733559655000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0911-6_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,8]]},"ISBN":["9789819609109","9789819609116"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0911-6_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,8]]},"assertion":[{"value":"8 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}