{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T08:58:09Z","timestamp":1785488289672,"version":"3.56.0"},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T00:00:00Z","timestamp":1765929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,12,17]]},"DOI":"10.1145\/3774521.3774553","type":"proceedings-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T07:34:24Z","timestamp":1785483264000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["SCoDA: Self-supervised Continual Domain Adaptation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0146-0320","authenticated-orcid":false,"given":"Chirayu","family":"Agrawal","sequence":"first","affiliation":[{"name":"Shiv Nadar Institution of Eminence, Delhi NCR, India, Delhi NCR, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2196-8980","authenticated-orcid":false,"given":"Snehasis","family":"Mukherjee","sequence":"additional","affiliation":[{"name":"Shiv Nadar Institution of Eminence, Delhi NCR, India, Delhi NCR, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00039"},{"key":"e_1_3_3_2_3_2","first-page":"108","volume-title":"ECCV","author":"Diamant I.","year":"2024","unstructured":"I. Diamant, A. Rosenfeld, I. Achituve, J. Goldberger, and A. Netzer. 2024. De-confusing Pseudo-Labels in Source-Free Domain Adaptation. In ECCV. Springer, 108\u2013125."},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00707"},{"key":"e_1_3_3_2_5_2","unstructured":"Haozhe Feng Zhaorui Yang Hesun Chen Tianyu Pang Chao Du Minfeng Zhu Wei Chen and Shuicheng Yan. 2023. CoSDA: Continual Source-Free Domain Adaptation. https:\/\/arxiv.org\/abs\/2304.06627"},{"key":"e_1_3_3_2_6_2","first-page":"21271","volume-title":"NeurIPS","author":"Grill Jean-Bastien","year":"2020","unstructured":"Jean-Bastien Grill, Florian Strub, Florent Altch\u00e9, Corentin Tallec, Pierre\u00a0H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo\u00a0Avila Pires, Zhaohan\u00a0Daniel Guo, Mohammad\u00a0Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, R\u00e9mi Munos, and Michal Valko. 2020. Bootstrap your own latent: A new approach to self-supervised learning. In NeurIPS. Advances in Neural Information Processing Systems, 21271\u201321284."},{"key":"e_1_3_3_2_7_2","unstructured":"Luu\u00a0Tung Hai Thinh\u00a0D. Le Zhicheng Ding Qing Tian and Truong-Son Hy. 2025. Topology-Guided Knowledge Distillation for Efficient Point Cloud Processing. https:\/\/arxiv.org\/pdf\/2505.08101"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"crossref","unstructured":"Y. Kim D. Cho K. Han P. Panda and S. Hong. 2021. Domain Adaptation Without Source Data. IEEE Transactions on Artificial Intelligence 2 6 (2021) 508\u2013518.","DOI":"10.1109\/TAI.2021.3110179"},{"key":"e_1_3_3_2_9_2","volume-title":"ICML","author":"Lee S.","year":"2022","unstructured":"S. Lee, D. Jung, J. Yim, and S. Yoon. 2022. Confidence Score for Source-Free Unsupervised Domain Adaptation. In ICML. PMLR."},{"key":"e_1_3_3_2_10_2","volume-title":"ICLR","author":"Li L.","year":"2023","unstructured":"L. Li, Y. Gu, X. Pu, J. Li, R. Pu, C. Ling, A.\u00a0J. McLeod, and B. Wang. 2023. When Source-free Domain Adaptation Meets Learning with Noisy Labels. In ICLR."},{"key":"e_1_3_3_2_11_2","volume-title":"ICML","author":"Liang J.","year":"2020","unstructured":"J. Liang, D. Hu, and J. Feng. 2020. Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation. In ICML. PMLR."},{"key":"e_1_3_3_2_12_2","unstructured":"J. Liang D. Hu Y. Wang R. He and J. Feng. 2022. Source-Data Absent Unsupervised Domain Adaptation Through Hypothesis Transfer and Labeling Transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence 44 11 (2022) 8602\u20138617."},{"key":"e_1_3_3_2_13_2","first-page":"228","volume-title":"ECCV","author":"Lyu M.","year":"2024","unstructured":"M. Lyu, T. Hao, X. Hu, H. Chen, Z. Lin, Z. Han, and G. Ding. 2024. Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence. In ECCV. Springer, 228\u2013246."},{"key":"e_1_3_3_2_14_2","first-page":"15720","volume-title":"CVPR","author":"Miles Roy","year":"2024","unstructured":"Roy Miles, Ismail Elezi, and Jiankang Deng. 2024. VkD: Improving Knowledge Distillation using Orthogonal Projections. In CVPR. IEEE, 15720\u201315730."},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02694"},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"},{"key":"e_1_3_3_2_17_2","first-page":"147","volume-title":"ECCV Workshops","author":"Singh A.","year":"2022","unstructured":"A. Singh and H. Wang. 2022. Simple unsupervised knowledge distillation with space similarity. In ECCV Workshops. Springer, 147\u2013164."},{"key":"e_1_3_3_2_18_2","first-page":"72","volume-title":"ECCV","author":"Song Y.","year":"2024","unstructured":"Y. Song, T.\u00a0S. Kim, L. Nam, T. Kooi, and C. Yoo. 2024. Is User Feedback Always Informative? Retrieval Latent Defending for Semisupervised Domain Adaptation without Source Data. In ECCV. Springer, 72\u201392."},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00198"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-023-01892-w"},{"key":"e_1_3_3_2_21_2","volume-title":"IROS","author":"Tang S.","year":"2022","unstructured":"S. Tang, Y. Shi, Z. Ma, J. Li, J. Lyu, Q. U, and J. Zhang. 2022. Model Adaptation through Hypothesis Transfer with Gradual Knowledge Distillation. In IROS. IEEE."},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02238"},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"crossref","unstructured":"S. Tang Y. Zou Z. Song J. Lyu C. Chen M. Ye S. Zhong and J. Zhang. 2022. Semantic Consistency Learning on Manifold for Source Datafree Unsupervised Domain Adaptation. Neural Networks 152 (2022) 467\u2013478.","DOI":"10.1016\/j.neunet.2022.05.015"},{"key":"e_1_3_3_2_24_2","first-page":"1195","volume-title":"NeurIPS","author":"Tarvainen A.","year":"2017","unstructured":"A. Tarvainen and H. Valpola. 2017. Mean teachers are better role models: Weight-averaged consistency targets for semi-supervised learning. In NeurIPS. Advances in Neural Information Processing Systems, 1195\u20131204."},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02232"},{"key":"e_1_3_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00888"},{"key":"e_1_3_3_2_28_2","first-page":"393","volume-title":"ECCV","author":"Xing B.","year":"2024","unstructured":"B. Xing, R. Yang, R. Guo, J. Shi, and W. Yue. 2024. Hierarchical Unsupervised Relation Distillation for Source Free Domain Adaptation. In ECCV. Springer, 393\u2013409."},{"key":"e_1_3_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00885"},{"key":"e_1_3_3_2_30_2","first-page":"29393","volume-title":"NeurIPS","author":"Yang S.","year":"2021","unstructured":"S. Yang, Y. Wang, K. Wang, S. Iui, and J. van\u00a0de Weijer. 2021. Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation. In NeurIPS. Advances in Neural Information Processing Systems, 29393\u201329405."},{"key":"e_1_3_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0420"},{"key":"e_1_3_3_2_32_2","doi-asserted-by":"crossref","unstructured":"L. Zhang L. Shen and C-S. Foo. 2025. Source-Free Domain Adaptation Guided by Vision and Vision-Language Pre-Training. International Journal of Computer Vision 133 (2025) 844\u2013866.","DOI":"10.1007\/s11263-024-02215-3"},{"key":"e_1_3_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0371"},{"key":"e_1_3_3_2_34_2","first-page":"2921","volume-title":"IJCAI","author":"Zhou Z.","year":"2021","unstructured":"Z. Zhou, Y. Shi, Z. Ma, J. Li, J. Lyu, Q. U, and J. Zhang. 2021. Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation. In IJCAI. IJCAI, 2921\u20132927."},{"key":"e_1_3_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.02392"}],"event":{"name":"ICVGIP 2025: Indian Conference on Computer Vision, Graphics, and Image Processing","location":"Mandi Himachal Pradesh India","acronym":"ICVGIP 2025"},"container-title":["Proceedings of the Sixteen Indian Conference on Computer Vision, Graphics and Image Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3774521.3774553","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T08:08:18Z","timestamp":1785485298000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3774521.3774553"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,17]]},"references-count":34,"alternative-id":["10.1145\/3774521.3774553","10.1145\/3774521"],"URL":"https:\/\/doi.org\/10.1145\/3774521.3774553","relation":{},"subject":[],"published":{"date-parts":[[2025,12,17]]},"assertion":[{"value":"2026-07-31","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}