{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:18:05Z","timestamp":1750220285233,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,12,19]],"date-time":"2021-12-19T00:00:00Z","timestamp":1639872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"SERB, Department of Science and Technology, Government of India"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,12,19]]},"DOI":"10.1145\/3490035.3490303","type":"proceedings-article","created":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T23:15:16Z","timestamp":1639523716000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Selective mixing and voting network for semi-supervised domain generalization"],"prefix":"10.1145","author":[{"given":"Ahmad","family":"Arfeen","sequence":"first","affiliation":[{"name":"Indian Institute of Science, Bangalore, Karnataka, India"}]},{"given":"Titir","family":"Dutta","sequence":"additional","affiliation":[{"name":"Indian Institute of Science, Bangalore, Karnataka, India"}]},{"given":"Soma","family":"Biswas","sequence":"additional","affiliation":[{"name":"Indian Institute of Science, Bangalore, Karnataka, India"}]}],"member":"320","published-online":{"date-parts":[[2021,12,19]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093303"},{"key":"e_1_3_2_1_2_1","unstructured":"G. Boqing Y. Shi F. Sha and K. Grauman. 2012. Geodesic flow kernel for unsupervised domain adaptation. In CVPR.  G. Boqing Y. Shi F. Sha and K. Grauman. 2012. Geodesic flow kernel for unsupervised domain adaptation. In CVPR."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.18"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"F. M. Carlucci A. D'Innocente S. Bucci B. Caputo and T. Tommasi. 2019. Domain generalization by solving jigsaw puzzles. In CVPR.  F. M. Carlucci A. D'Innocente S. Bucci B. Caputo and T. Tommasi. 2019. Domain generalization by solving jigsaw puzzles. In CVPR.","DOI":"10.1109\/CVPR.2019.00233"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00189"},{"volume-title":"International Conference on Learning Representations.","year":"2020","author":"Chou Yu-Ying","key":"e_1_3_2_1_6_1"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"A. D'Innocente and B. Caputo. 2018. Domain generalization with domain-specific aggregation module. In GCPR.  A. D'Innocente and B. Caputo. 2018. Domain generalization with domain-specific aggregation module. In GCPR.","DOI":"10.1007\/978-3-030-12939-2_14"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3454866"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01411"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045244"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.2946704"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305381.3305518"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"C. Huang Y. Li C. C. Loy and X Tang. 2016. Learning deep representation for imbalanced classification. In CVPR.  C. Huang Y. Li C. C. Loy and X Tang. 2016. Learning deep representation for imbalanced classification. In CVPR.","DOI":"10.1109\/CVPR.2016.580"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.5555\/3042817.3042820"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.5555\/2999134.2999257"},{"volume-title":"Temporal ensembling for semi-supervised learning. arXiv preprint arXiv:1610.02242","year":"2016","author":"Laine Samuli","key":"e_1_3_2_1_17_1"},{"volume":"3","volume-title":"Workshop on challenges in representation learning, ICML","author":"Dong-Hyun","key":"e_1_3_2_1_18_1"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"D. Li Y. Yang Y. Z. Song and T. M. Hospedales. 2017. Deeper broader and artier domain generalization. In ICCV.  D. Li Y. Yang Y. Z. Song and T. M. Hospedales. 2017. Deeper broader and artier domain generalization. In ICCV.","DOI":"10.1109\/ICCV.2017.591"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.5555\/3504035.3504462"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00566"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/3157096.3157149"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045130"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"T. Maatsura and T. Harada. 2020. Domain generalization using a mixture of multiple latent domains. In AAAI.  T. Maatsura and T. Harada. 2020. Domain generalization using a mixture of multiple latent domains. In AAAI.","DOI":"10.1609\/aaai.v34i07.6846"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455008"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01257"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107124"},{"volume-title":"Meta-learning for semi-supervised few-shot classification. arXiv preprint arXiv:1803.00676","year":"2018","author":"Ren Mengye","key":"e_1_3_2_1_28_1"},{"volume-title":"Regularization with stochastic transformations and perturbations for deep semi-supervised learning. Advances in neural information processing systems 29","year":"2016","author":"Sajjadi Mehdi","key":"e_1_3_2_1_29_1"},{"volume-title":"Proceedings, Part XXII 16","year":"2020","author":"Seo Seonguk","key":"e_1_3_2_1_30_1"},{"volume-title":"Domain generalization via semi-supervised meta learning. arXiv preprint arXiv:2009.12658","year":"2020","author":"Sharifi-Noghabi Hossein","key":"e_1_3_2_1_31_1"},{"volume-title":"Fixmatch: Simplifying semi-supervised learning with consistency and confidence. arXiv preprint arXiv:2001.07685","year":"2020","author":"Sohn Kihyuk","key":"e_1_3_2_1_32_1"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294885"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"volume-title":"Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474","year":"2014","author":"Tzeng Eric","key":"e_1_3_2_1_36_1"},{"volume-title":"International Conference on Machine Learning. PMLR, 6438--6447","year":"2019","author":"Verma Vikas","key":"e_1_3_2_1_37_1"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"N. Vo L. Jiang C. Sun K. Murphy L. J. Li L. Fei-Fei and J. Hays. 2019. Composing Text and Image for Image Retrieval - An Empirical Odyssey. In CVPR.  N. Vo L. Jiang C. Sun K. Murphy L. J. Li L. Fei-Fei and J. Hays. 2019. Composing Text and Image for Image Retrieval - An Empirical Odyssey. In CVPR.","DOI":"10.1109\/CVPR.2019.00660"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"K. Wang J. H. Liew Y. Zou D. Zhou and J. Feng. 2019. PANet: few-shot image semantic segmentation with prototype alignment. In ICCV.  K. Wang J. H. Liew Y. Zou D. Zhou and J. Feng. 2019. PANet: few-shot image semantic segmentation with prototype alignment. In ICCV.","DOI":"10.1109\/ICCV.2019.00929"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58545-7_10"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053273"},{"key":"e_1_3_2_1_42_1","unstructured":"L. Ya X. Tian M. Gong Y. Liu T. Liu K. Zhang and D. Tao. 2018. Deep domain generalization via conditional invariant adversarial networks. In ECCV.  L. Ya X. Tian M. Gong Y. Liu T. Liu K. Zhang and D. Tao. 2018. Deep domain generalization via conditional invariant adversarial networks. In ECCV."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.107987"},{"volume-title":"mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412","year":"2017","author":"Zhang Hongyi","key":"e_1_3_2_1_44_1"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"crossref","unstructured":"H. Zhang S. Liu C. Zhang W. Ren R. Wang and X. Cao. 2016. Sketchnet: sketch classification with web images. In CVPR.  H. Zhang S. Liu C. Zhang W. Ren R. Wang and X. Cao. 2016. Sketchnet: sketch classification with web images. In CVPR.","DOI":"10.1109\/CVPR.2016.125"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","unstructured":"J. Zhang F. Shen L. Liu F. Zhu M. Yu L. Shao H. Tao Shen and L. Van Gool. 2018. Generative domain-migration hashing for sketch-to-image retrieval. In ECCV.  J. Zhang F. Shen L. Liu F. Zhu M. Yu L. Shao H. Tao Shen and L. Van Gool. 2018. Generative domain-migration hashing for sketch-to-image retrieval. In ECCV.","DOI":"10.1007\/978-3-030-01216-8_19"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58548-8_45"},{"key":"e_1_3_2_1_48_1","unstructured":"S. Zhao M. Gong T. Liu H. Fu and D. Tao. 2020. Domain generalization via entropy regularization. In NeurIPS.  S. Zhao M. Gong T. Liu H. Fu and D. Tao. 2020. Domain generalization via entropy regularization. In NeurIPS."},{"volume-title":"Domain generalization with mixstyle. arXiv preprint arXiv:2104.02008","year":"2021","author":"Zhou Kaiyang","key":"e_1_3_2_1_49_1"}],"event":{"name":"ICVGIP '21: Indian Conference on Computer Vision, Graphics and Image Processing","acronym":"ICVGIP '21","location":"Jodhpur India"},"container-title":["Proceedings of the Twelfth Indian Conference on Computer Vision, Graphics and Image Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3490035.3490303","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3490035.3490303","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:31:23Z","timestamp":1750188683000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3490035.3490303"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,19]]},"references-count":49,"alternative-id":["10.1145\/3490035.3490303","10.1145\/3490035"],"URL":"https:\/\/doi.org\/10.1145\/3490035.3490303","relation":{},"subject":[],"published":{"date-parts":[[2021,12,19]]},"assertion":[{"value":"2021-12-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}