{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T16:52:04Z","timestamp":1781542324789,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"the National Natural Science Foundation of China (Regional Project)","award":["62466056"],"award-info":[{"award-number":["62466056"]}]},{"name":"the Tianchi Young Talent Doctoral Program","award":["51052501824"],"award-info":[{"award-number":["51052501824"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,6,16]]},"DOI":"10.1145\/3805622.3810725","type":"proceedings-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:42:57Z","timestamp":1781534577000},"page":"1404-1412","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["STEP: Stable Gradient Projection for Continual Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7115-4740","authenticated-orcid":false,"given":"Longlong","family":"Zhai","sequence":"first","affiliation":[{"name":"Xinjiang University, \u00dcr\u00fcmqi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6159-4525","authenticated-orcid":false,"given":"Jiao","family":"Tian","sequence":"additional","affiliation":[{"name":"Xinjiang University, \u00dcr\u00fcmqi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8615-139X","authenticated-orcid":false,"given":"Feng","family":"Yan","sequence":"additional","affiliation":[{"name":"Xinjiang University, \u00dcr\u00fcmqi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1672-7821","authenticated-orcid":false,"given":"Lei","family":"Su","sequence":"additional","affiliation":[{"name":"Xinjiang University, \u00dcr\u00fcmqi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5011-8697","authenticated-orcid":false,"given":"Yanjun","family":"Qin","sequence":"additional","affiliation":[{"name":"Xinjiang University, \u00dcr\u00fcmqi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2135-5084","authenticated-orcid":false,"given":"Shaochen","family":"Jiang","sequence":"additional","affiliation":[{"name":"Xinjiang University, \u00dcr\u00fcmqi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0003-5126","authenticated-orcid":false,"given":"Chong","family":"Peng","sequence":"additional","affiliation":[{"name":"Ocean University of China, Qingdao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2934-6339","authenticated-orcid":false,"given":"Panpan","family":"Zheng","sequence":"additional","affiliation":[{"name":"Xinjiang University, \u00dcr\u00fcmqi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_9"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51701.2025.00080"},{"key":"e_1_3_3_1_4_2","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 33 (2020) 15920\u201315930."},{"key":"e_1_3_3_1_5_2","unstructured":"Arslan Chaudhry Marcus Rohrbach Mohamed Elhoseiny Thalaiyasingam Ajanthan Puneet\u00a0K Dokania Philip\u00a0HS Torr and Marc\u2019Aurelio Ranzato. 2019. On tiny episodic memories in continual learning. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1902.10486 (2019)."},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548054"},{"key":"e_1_3_3_1_7_2","unstructured":"Zhiyi Chen and Tong Lin*. 2020. Revisiting Gradient Episodic Memory for Continual Learning. https:\/\/openreview.net\/forum?id=H1g79ySYvB"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58621-8_23"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"crossref","unstructured":"Zhaoru Guo Yongquan Xue Yongming Li Shengchang Wang Yue Han and Panpan Zheng. 2025. ASC-Seg: Adaptive Structure Alignment and Cross-Scale Decoding for Medical Image Segmentation. 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2025) 3640\u20133643. https:\/\/api.semanticscholar.org\/CorpusID:285174982","DOI":"10.1109\/BIBM66473.2025.11356468"},{"key":"e_1_3_3_1_10_2","volume-title":"The Fourteenth International Conference on Learning Representations","author":"Kim Dongjun","year":"2026","unstructured":"Dongjun Kim, Seohyeon Cha, Huancheng Chen, Chianing Wang, and Haris Vikalo. 2026. Quantized Gradient Projection for Memory-Efficient Continual Learning. In The Fourteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=xJtxpJ6QdD"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"crossref","unstructured":"James Kirkpatrick Razvan Pascanu Neil Rabinowitz Joel Veness Guillaume Desjardins Andrei\u00a0A Rusu Kieran Milan John Quan Tiago Ramalho Agnieszka Grabska-Barwinska et\u00a0al. 2017. Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114 13 (2017) 3521\u20133526.","DOI":"10.1073\/pnas.1611835114"},{"key":"e_1_3_3_1_12_2","volume-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky Alex","year":"2009","unstructured":"Alex Krizhevsky and Geoffrey Hinton. 2009. Learning multiple layers of features from tiny images. Technical Report. University of Toronto. https:\/\/www.cs.toronto.edu\/\u00a0kriz\/learning-features-2009-TR.pdf"},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"publisher","unstructured":"Pratibha Kumari Joohi Chauhan Afshin Bozorgpour Boqiang Huang Reza Azad and Dorit Merhof. 2025. Continual learning in medical image analysis: A comprehensive review of recent advancements and future prospects. Medical Image Analysis 106 (2025) 103730. 10.1016\/j.media.2025.103730","DOI":"10.1016\/j.media.2025.103730"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_37"},{"key":"e_1_3_3_1_15_2","volume-title":"International Conference on Learning Representations","author":"Lin Sen","year":"2022","unstructured":"Sen Lin, Li Yang, Deliang Fan, and Junshan Zhang. 2022. TRGP: Trust Region Gradient Projection for Continual Learning. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=iEvAf8i6JjO"},{"key":"e_1_3_3_1_16_2","volume-title":"Advances in Neural Information Processing Systems","author":"Lopez-Paz David","year":"2017","unstructured":"David Lopez-Paz and Marc'\u00a0Aurelio Ranzato. 2017. Gradient Episodic Memory for Continual Learning. In Advances in Neural Information Processing Systems , I.\u00a0Guyon, U.\u00a0Von Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.), Vol.\u00a030. Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/f87522788a2be2d171666752f97ddebb-Paper.pdf"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00810"},{"key":"e_1_3_3_1_18_2","volume-title":"The Thirteenth International Conference on Learning Representations","author":"Ma\u2019sum Muhammad\u00a0Anwar","year":"2025","unstructured":"Muhammad\u00a0Anwar Ma\u2019sum, Mahardhika Pratama, Savitha Ramasamy, Lin Liu, H Habibullah, and Ryszard Kowalczyk. 2025. Vision and Language Synergy for Rehearsal Free Continual Learning. In The Thirteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=9aZ2ixiYGd"},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"publisher","unstructured":"Marcin Pietron Kamil Faber Dominik \u017burek and Roberto Corizzo. 2025. TinySubNets: An Efficient and Low Capacity Continual Learning Strategy. Proceedings of the AAAI Conference on Artificial Intelligence 39 19 (Apr. 2025) 19913\u201319920. 10.1609\/aaai.v39i19.34193","DOI":"10.1609\/aaai.v39i19.34193"},{"key":"e_1_3_3_1_20_2","volume-title":"The Twelfth International Conference on Learning Representations","author":"Qiao Jingyang","year":"2024","unstructured":"Jingyang Qiao, zhizhong zhang, Xin Tan, Chengwei Chen, Yanyun Qu, Yong Peng, and Yuan Xie. 2024. Prompt Gradient Projection for Continual Learning. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=EH2O3h7sBI"},{"key":"e_1_3_3_1_21_2","unstructured":"Zihan Qiu Zekun Wang Bo Zheng Zeyu Huang Kaiyue Wen Songlin Yang Rui Men Le Yu Fei Huang Suozhi Huang Dayiheng Liu Jingren Zhou and Junyang Lin. 2025. Gated Attention for Large Language Models: Non-linearity Sparsity and Attention-Sink-Free. arxiv:https:\/\/arXiv.org\/abs\/2505.06708\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2505.06708"},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.587"},{"key":"e_1_3_3_1_23_2","unstructured":"Gobinda Saha Isha Garg and Kaushik Roy. 2021. Gradient projection memory for continual learning. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2103.09762 (2021)."},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26157"},{"key":"e_1_3_3_1_25_2","first-page":"4548","volume-title":"International conference on machine learning","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. PMLR, 4548\u20134557."},{"key":"e_1_3_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3731715.3733308"},{"key":"e_1_3_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-5529-2_8"},{"key":"e_1_3_3_1_28_2","unstructured":"Liyuan Wang Mingtian Zhang Zhongfan Jia Qian Li Chenglong Bao Kaisheng Ma Jun Zhu and Yi Zhong. 2021. Afec: Active forgetting of negative transfer in continual learning. Advances in Neural Information Processing Systems 34 (2021) 22379\u201322391."},{"key":"e_1_3_3_1_29_2","unstructured":"Shipeng Wang Xiaorong Li Jian Sun and Zongben Xu. 2024. Training networks in null space of feature covariance with self-supervision for incremental learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)."},{"key":"e_1_3_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3746027.3755440"},{"key":"e_1_3_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3664647.3680605"},{"key":"e_1_3_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00518"},{"key":"e_1_3_3_1_33_2","doi-asserted-by":"publisher","unstructured":"Enneng Yang Li Shen Zhenyi Wang Shiwei Liu Guibing Guo Xingwei Wang and Dacheng Tao. 2025. Revisiting Flatness-Aware Optimization in Continual Learning With Orthogonal Gradient Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 47 5 (2025) 3895\u20133907. 10.1109\/TPAMI.2025.3539019","DOI":"10.1109\/TPAMI.2025.3539019"},{"key":"e_1_3_3_1_34_2","doi-asserted-by":"publisher","unstructured":"Zeyuan Yang Zonghan Yang Yichen Liu Peng Li and Yang Liu. 2023. Restricted orthogonal gradient projection for continual learning. AI Open 4 (2023) 98\u2013110. 10.1016\/j.aiopen.2023.08.010","DOI":"10.1016\/j.aiopen.2023.08.010"},{"key":"e_1_3_3_1_35_2","volume-title":"International Conference on Learning Representations","author":"Yoon Jaehong","year":"2020","unstructured":"Jaehong Yoon, Saehoon Kim, Eunho Yang, and Sung\u00a0Ju Hwang. 2020. Scalable and Order-robust Continual Learning with Additive Parameter Decomposition. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=r1gdj2EKPB"},{"key":"e_1_3_3_1_36_2","doi-asserted-by":"publisher","unstructured":"Longlong Zhai Jinrong He Zhaokui Li and Yingzhou Bi. 2025. Few-shot tire tracks recognition based on metric learning. Engineering Applications of Artificial Intelligence 156 (2025) 111188. 10.1016\/j.engappai.2025.111188","DOI":"10.1016\/j.engappai.2025.111188"},{"key":"e_1_3_3_1_37_2","doi-asserted-by":"publisher","unstructured":"Panpan Zheng Shuhan Yuan and Xintao Wu. 2019. SAFE: A Neural Survival Analysis Model for Fraud Early Detection. Proceedings of the AAAI Conference on Artificial Intelligence 33 01 (Jul. 2019) 1278\u20131285. 10.1609\/aaai.v33i01.33011278","DOI":"10.1609\/aaai.v33i01.33011278"},{"key":"e_1_3_3_1_38_2","doi-asserted-by":"publisher","unstructured":"Panpan Zheng Shuhan Yuan Xintao Wu Jun Li and Aidong Lu. 2019. One-Class Adversarial Nets for Fraud Detection. Proceedings of the AAAI Conference on Artificial Intelligence 33 01 (Jul. 2019) 1286\u20131293. 10.1609\/aaai.v33i01.33011286","DOI":"10.1609\/aaai.v33i01.33011286"}],"event":{"name":"ICMR '26: International Conference on Multimedia Retrieval","location":"Amsterdam The Netherlands","acronym":"ICMR '26","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 2026 International Conference on Multimedia Retrieval"],"original-title":[],"deposited":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T15:53:31Z","timestamp":1781538811000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3805622.3810725"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,15]]},"references-count":37,"alternative-id":["10.1145\/3805622.3810725","10.1145\/3805622"],"URL":"https:\/\/doi.org\/10.1145\/3805622.3810725","relation":{},"subject":[],"published":{"date-parts":[[2026,6,15]]},"assertion":[{"value":"2026-06-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}