{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:39:28Z","timestamp":1742960368400,"version":"3.40.3"},"publisher-location":"Cham","reference-count":46,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031729720"},{"type":"electronic","value":"9783031729737"}],"license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"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\/978-3-031-72973-7_23","type":"book-chapter","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T14:03:04Z","timestamp":1730383384000},"page":"393-409","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hierarchical Unsupervised Relation Distillation for\u00a0Source Free Domain Adaptation"],"prefix":"10.1007","author":[{"given":"Bowei","family":"Xing","sequence":"first","affiliation":[]},{"given":"Xianghua","family":"Ying","sequence":"additional","affiliation":[]},{"given":"Ruibin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ruohao","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Ji","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Wenzhen","family":"Yue","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"issue":"9","key":"23_CR1","first-page":"5516","volume":"44","author":"S Bucci","year":"2022","unstructured":"Bucci, S., D\u2019Innocente, A., Liao, Y., Carlucci, F.M., Caputo, B., Tommasi, T.: Self-supervised learning across domains. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 5516\u20135528 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Chen, D., Wang, D., Darrell, T., Ebrahimi, S.: Contrastive test-time adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 295\u2013305 (2022)","DOI":"10.1109\/CVPR52688.2022.00039"},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhu, X., Li, Y., Li, Y., Wei, Y., Fang, H.: Contrast and clustering: learning neighborhood pair representation for source-free domain adaptation. arXiv preprint arXiv:2301.13428 (2023)","DOI":"10.2139\/ssrn.4412854"},{"key":"23_CR4","unstructured":"Fang, Y., Yap, P.T., Lin, W., Zhu, H., Liu, M.: Source-free unsupervised domain adaptation: a survey. arXiv preprint arXiv:2301.00265 (2022)"},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Farahani, A., Voghoei, S., Rasheed, K., Arabnia, H.R.: A brief review of domain adaptation. arXiv preprint arXiv:2010.03978 (2020)","DOI":"10.1007\/978-3-030-71704-9_65"},{"key":"23_CR6","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","volume":"129","author":"J Gou","year":"2021","unstructured":"Gou, J., Yu, B., Maybank, S.J., Tao, D.: Knowledge distillation: a survey. Int. J. Comput. Vision 129, 1789\u20131819 (2021)","journal-title":"Int. J. Comput. Vision"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Guillory, D., Shankar, V., Ebrahimi, S., Darrell, T., Schmidt, L.: Predicting with confidence on unseen distributions. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1134\u20131144 (2021)","DOI":"10.1109\/ICCV48922.2021.00117"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"23_CR9","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Jin, Y., Wang, X., Long, M., Wang, J.: Minimum class confusion for versatile domain adaptation. In: Proceedings of the European Conference on Computer Vision, pp. 464\u2013480 (2020)","DOI":"10.1007\/978-3-030-58589-1_28"},{"key":"23_CR11","first-page":"17173","volume":"35","author":"M Jing","year":"2022","unstructured":"Jing, M., Zhen, X., Li, J., Snoek, C.: Variational model perturbation for source-free domain adaptation. Adv. Neural. Inf. Process. Syst. 35, 17173\u201317187 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"Karim, N., Mithun, N.C., Rajvanshi, A., Chiu, H.P., Samarasekera, S., Rahnavard, N.: C-sfda: a curriculum learning aided self-training framework for efficient source free domain adaptation. arXiv preprint arXiv:2303.17132 (2023)","DOI":"10.1109\/CVPR52729.2023.02310"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Kundu, J.N., Bhambri, S., Kulkarni, A., Sarkar, H., Jampani, V., Babu, R.V.: Concurrent subsidiary supervision for unsupervised source-free domain adaptation. In: European Conference on Computer Vision, pp. 177\u2013194 (2022)","DOI":"10.1007\/978-3-031-20056-4_11"},{"key":"23_CR14","unstructured":"Lee, J., Jung, D., Yim, J., Yoon, S.: Confidence score for source-free unsupervised domain adaptation. In: Proceedings of the International Conference on Machine Learning, pp. 12365\u201312377 (2022)"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Li, R., Jiao, Q., Cao, W., Wong, H.S., Wu, S.: Model adaptation: unsupervised domain adaptation without source data. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9641\u20139650 (2020)","DOI":"10.1109\/CVPR42600.2020.00966"},{"key":"23_CR16","unstructured":"Li, S., et al.: Semantic concentration for domain adaptation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9102\u20139111 (2021)"},{"key":"23_CR17","unstructured":"Liang, J., He, R., Tan, T.: A comprehensive survey on test-time adaptation under distribution shifts. arXiv preprint arXiv:2303.15361 (2023)"},{"key":"23_CR18","unstructured":"Liang, J., Hu, D., Feng, J.: Do we really need to access the source data? Source hypothesis transfer for unsupervised domain adaptation. In: Proceedings of the International Conference on Machine Learning, pp. 6028\u20136039 (2020)"},{"issue":"11","key":"23_CR19","first-page":"8602","volume":"44","author":"J Liang","year":"2021","unstructured":"Liang, J., Hu, D., Wang, Y., He, R., Feng, J.: Source data-absent unsupervised domain adaptation through hypothesis transfer and labeling transfer. IEEE Trans. Pattern Anal. Mach. Intell. 44(11), 8602\u20138617 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"23_CR20","doi-asserted-by":"crossref","unstructured":"Litrico, M., Del\u00a0Bue, A., Morerio, P.: Guiding pseudo-labels with uncertainty estimation for source-free unsupervised domain adaptation. arXiv preprint arXiv:2303.03770 (2023)","DOI":"10.1109\/CVPR52729.2023.00738"},{"key":"23_CR21","first-page":"1640","volume":"31","author":"M Long","year":"2018","unstructured":"Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. Adv. Neural. Inf. Process. Syst. 31, 1640\u20131650 (2018)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"23_CR22","unstructured":"Morerio, P., Cavazza, J., Murino, V.: Minimal-entropy correlation alignment for unsupervised deep domain adaptation. In: Proceedings of the International Conference on Learning Representations, pp. 1\u201312 (2018)"},{"key":"23_CR23","doi-asserted-by":"crossref","unstructured":"Na, J., Jung, H., Chang, H.J., Hwang, W.: Fixbi: bridging domain spaces for unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1094\u20131103 (2021)","DOI":"10.1109\/CVPR46437.2021.00115"},{"key":"23_CR24","doi-asserted-by":"publisher","first-page":"2033","DOI":"10.1109\/TIP.2023.3258753","volume":"32","author":"J Pei","year":"2023","unstructured":"Pei, J., Jiang, Z., Men, A., Chen, L., Liu, Y., Chen, Q.: Uncertainty-induced transferability representation for source-free unsupervised domain adaptation. IEEE Trans. Image Process. 32, 2033\u20132048 (2023)","journal-title":"IEEE Trans. Image Process."},{"key":"23_CR25","doi-asserted-by":"crossref","unstructured":"Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., Wang, B.: Moment matching for multi-source domain adaptation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1406\u20131415 (2019)","DOI":"10.1109\/ICCV.2019.00149"},{"key":"23_CR26","unstructured":"Peng, X., Usman, B., Kaushik, N., Hoffman, J., Wang, D., Saenko, K.: Visda: the visual domain adaptation challenge. arXiv preprint arXiv:1710.06924 (2017)"},{"key":"23_CR27","doi-asserted-by":"crossref","unstructured":"Qu, S., Chen, G., Zhang, J., Li, Z., He, W., Tao, D.: BMD: a general class-balanced multicentric dynamic prototype strategy for source-free domain adaptation. In: European Conference on Computer Vision, pp. 165\u2013182 (2022)","DOI":"10.1007\/978-3-031-19830-4_10"},{"key":"23_CR28","doi-asserted-by":"crossref","unstructured":"Quinonero-Candela, J., Sugiyama, M., Schwaighofer, A., Lawrence, N.D.: Dataset Shift in Machine Learning. MIT Press (2008)","DOI":"10.7551\/mitpress\/9780262170055.001.0001"},{"key":"23_CR29","doi-asserted-by":"crossref","unstructured":"Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Proceedings of the European Conference on Computer Vision, pp. 213\u2013226 (2010)","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"23_CR30","doi-asserted-by":"crossref","unstructured":"Saito, K., Watanabe, K., Ushiku, Y., Harada, T.: Maximum classifier discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3723\u20133732 (2018)","DOI":"10.1109\/CVPR.2018.00392"},{"key":"23_CR31","doi-asserted-by":"crossref","unstructured":"Sun, B., Feng, J., Saenko, K.: Correlation alignment for unsupervised domain adaptation. In: Domain Adaptation in Computer Vision Applications, pp. 153\u2013171 (2017)","DOI":"10.1007\/978-3-319-58347-1_8"},{"key":"23_CR32","doi-asserted-by":"crossref","unstructured":"Tang, H., Chen, K., Jia, K.: Unsupervised domain adaptation via structurally regularized deep clustering. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8725\u20138735 (2020)","DOI":"10.1109\/CVPR42600.2020.00875"},{"key":"23_CR33","doi-asserted-by":"crossref","unstructured":"Venkateswara, H., Eusebio, J., Chakraborty, S., Panchanathan, S.: Deep hashing network for unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5018\u20135027 (2017)","DOI":"10.1109\/CVPR.2017.572"},{"key":"23_CR34","unstructured":"Wang, D., Shelhamer, E., Liu, S., Olshausen, B., Darrell, T.: Tent: fully test-time adaptation by entropy minimization. In: Proceedings of the International Conference on Learning Representations (2021)"},{"key":"23_CR35","unstructured":"Wu, C., Cao, G., Li, Y., Xi, X., Cao, W., Wang, H.: When source-free domain adaptation meets label propagation. arXiv preprint arXiv:2301.08413 (2023)"},{"key":"23_CR36","doi-asserted-by":"crossref","unstructured":"Xia, H., Zhao, H., Ding, Z.: Adaptive adversarial network for source-free domain adaptation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9010\u20139019 (2021)","DOI":"10.1109\/ICCV48922.2021.00888"},{"key":"23_CR37","doi-asserted-by":"crossref","unstructured":"Yang, S., Wang, Y., Van De\u00a0Weijer, J., Herranz, L., Jui, S.: Generalized source-free domain adaptation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8978\u20138987 (2021)","DOI":"10.1109\/ICCV48922.2021.00885"},{"key":"23_CR38","unstructured":"Yang, S., Wang, Y., Wang, K., Jui, S., Weijer, J.V.D.: Attracting and dispersing: a simple approach for source-free domain adaptation. Adv. Neural Inf. Process. Syst. 35, 5802\u20135815 (2022)"},{"key":"23_CR39","first-page":"29393","volume":"34","author":"S Yang","year":"2021","unstructured":"Yang, S., Yaxing, W., van de Weijer, J., Herranz, L., Jui, S.: Exploiting the intrinsic neighborhood structure for source-free domain adaptation. Adv. Neural. Inf. Process. Syst. 34, 29393\u201329405 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"23_CR40","unstructured":"Yu, Z., Li, J., Du, Z., Zhu, L., Shen, H.: A comprehensive survey on source-free domain adaptation. arXiv preprint arXiv:2302.11803 (2023)"},{"issue":"8","key":"23_CR41","first-page":"4388","volume":"44","author":"L Zhang","year":"2021","unstructured":"Zhang, L., Bao, C., Ma, K.: Self-distillation: towards efficient and compact neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 44(8), 4388\u20134403 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"23_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, L., Song, J., Gao, A., Chen, J., Bao, C., Ma, K.: Be your own teacher: improve the performance of convolutional neural networks via self distillation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3713\u20133722 (2019)","DOI":"10.1109\/ICCV.2019.00381"},{"key":"23_CR43","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, Z., He, W.: Class relationship embedded learning for source-free unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7619\u20137629 (2023)","DOI":"10.1109\/CVPR52729.2023.00736"},{"key":"23_CR44","unstructured":"Zhang, Y., Liu, T., Long, M., Jordan, M.: Bridging theory and algorithm for domain adaptation. In: Proceedings of the International Conference on Machine Learning, pp. 7404\u20137413 (2019)"},{"key":"23_CR45","unstructured":"Zhang, Z., et al.: Divide and contrast: source-free domain adaptation via adaptive contrastive learning. Adv. Neural. Inf. Process. Syst. 35, 5137\u20135149 (2022)"},{"key":"23_CR46","doi-asserted-by":"crossref","unstructured":"Zhao, B., Cui, Q., Song, R., Qiu, Y., Liang, J.: Decoupled knowledge distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11953\u201311962 (2022)","DOI":"10.1109\/CVPR52688.2022.01165"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72973-7_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T15:00:11Z","timestamp":1739631611000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72973-7_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,1]]},"ISBN":["9783031729720","9783031729737"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72973-7_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,1]]},"assertion":[{"value":"1 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}