{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T03:44:14Z","timestamp":1771645454217,"version":"3.50.1"},"publisher-location":"Cham","reference-count":46,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031250552","type":"print"},{"value":"9783031250569","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-25056-9_16","type":"book-chapter","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T12:09:56Z","timestamp":1676376596000},"page":"234-250","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["How Well Do Vision Transformers (VTs) Transfer to\u00a0the\u00a0Non-natural Image Domain? An\u00a0Empirical Study Involving Art Classification"],"prefix":"10.1007","author":[{"given":"Vincent","family":"Tonkes","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthia","family":"Sabatelli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,15]]},"reference":[{"issue":"1","key":"16_CR1","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1093\/mnras\/sty1398","volume":"479","author":"S Ackermann","year":"2018","unstructured":"Ackermann, S., Schawinski, K., Zhang, C., Weigel, A.K., Turp, M.D.: Using transfer learning to detect galaxy mergers. Mon. Not. R. Astron. Soc. 479(1), 415\u2013425 (2018)","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Bai, Z., Nakashima, Y., Garcia, N.: Explain me the painting: multi-topic knowledgeable art description generation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5422\u20135432 (2021)","DOI":"10.1109\/ICCV48922.2021.00537"},{"key":"16_CR3","unstructured":"Bao, H., Dong, L., Piao, S., Wei, F.: BEiT: BERT pre-training of image transformers. In: ICLR 2022 (2022)"},{"key":"16_CR4","unstructured":"Bengio, Y.: Deep learning of representations for unsupervised and transfer learning. In: Proceedings of ICML Workshop on Unsupervised and Transfer Learning, pp. 17\u201336. JMLR Workshop and Conference Proceedings (2012)"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Caron, M., Touvron, H., Misra, I., J\u00e9gou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9650\u20139660 (2021)","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Chen, X., Xie, S., He, K.: An empirical study of training self-supervised vision transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9640\u20139649 (2021)","DOI":"10.1109\/ICCV48922.2021.00950"},{"issue":"9","key":"16_CR7","doi-asserted-by":"publisher","first-page":"2491","DOI":"10.1109\/TMM.2018.2801718","volume":"20","author":"WT Chu","year":"2018","unstructured":"Chu, W.T., Wu, Y.L.: Image style classification based on learnt deep correlation features. IEEE Trans. Multimedia 20(9), 2491\u20132502 (2018)","journal-title":"IEEE Trans. Multimedia"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"16_CR9","unstructured":"Dosovitskiy, A., et al.: An image is worth 16$$\\,\\times \\,$$16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"16_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115519","volume":"184","author":"LT Duong","year":"2021","unstructured":"Duong, L.T., Le, N.H., Tran, T.B., Ngo, V.M., Nguyen, P.T.: Detection of tuberculosis from chest x-ray images: boosting the performance with vision transformer and transfer learning. Expert Syst. Appl. 184, 115519 (2021)","journal-title":"Expert Syst. Appl."},{"key":"16_CR11","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.patrec.2020.02.017","volume":"133","author":"M Fiorucci","year":"2020","unstructured":"Fiorucci, M., Khoroshiltseva, M., Pontil, M., Traviglia, A., Del Bue, A., James, S.: Machine learning for cultural heritage: a survey. Pattern Recogn. Lett. 133, 102\u2013108 (2020)","journal-title":"Pattern Recogn. Lett."},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Gonthier, N., Gousseau, Y., Ladjal, S., Bonfait, O.: Weakly supervised object detection in artworks. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)","DOI":"10.1007\/978-3-030-11012-3_53"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"16_CR14","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"16_CR15","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc. (2012)"},{"key":"16_CR16","unstructured":"Li, Y., Xie, S., Chen, X., Dollar, P., He, K., Girshick, R.: Benchmarking detection transfer learning with vision transformers. arXiv preprint arXiv:2111.11429 (2021)"},{"key":"16_CR17","unstructured":"Liu, Y., Sangineto, E., Bi, W., Sebe, N., Lepri, B., Nadai, M.: Efficient training of visual transformers with small datasets. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol. 34, pp. 23818\u201323830. Curran Associates, Inc. (2021). proceedings.neurips.cc\/paper\/2021\/file\/c81e155d85dae5430a8cee6f2242e82c-Paper.pdf"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. arXiv preprint arXiv:2201.03545 (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"16_CR20","unstructured":"Masters, D., Luschi, C.: Revisiting small batch training for deep neural networks. arXiv preprint arXiv:1804.07612 (2018)"},{"key":"16_CR21","unstructured":"Matsoukas, C., Haslum, J.F., S\u00f6derberg, M., Smith, K.: Is it time to replace CNNs with transformers for medical images? arXiv preprint arXiv:2108.09038 (2021)"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Mensink, T., van Gemert, J.: The Rijksmuseum challenge: museum-centered visual recognition. In: ACM International Conference on Multimedia Retrieval (ICMR) (2014)","DOI":"10.1145\/2578726.2578791"},{"issue":"4","key":"16_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3458885","volume":"14","author":"F Milani","year":"2021","unstructured":"Milani, F., Fraternali, P.: A dataset and a convolutional model for iconography classification in paintings. J. Comput. Cult. Heritage (JOCCH) 14(4), 1\u201318 (2021)","journal-title":"J. Comput. Cult. Heritage (JOCCH)"},{"key":"16_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101794","volume":"65","author":"S Minaee","year":"2020","unstructured":"Minaee, S., Kafieh, R., Sonka, M., Yazdani, S., Soufi, G.J.: Deep-Covid: Predicting Covid-19 from chest x-ray images using deep transfer learning. Med. Image Anal. 65, 101794 (2020)","journal-title":"Med. Image Anal."},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Mormont, R., Geurts, P., Maree, R.: Comparison of deep transfer learning strategies for digital pathology. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2018","DOI":"10.1109\/CVPRW.2018.00303"},{"issue":"10","key":"16_CR26","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2009)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"16_CR27","unstructured":"Paszke, A., et al.: Automatic differentiation in pytorch (2017)"},{"key":"16_CR28","unstructured":"Sabatelli, M.: Contributions to deep transfer learning: from supervised to reinforcement learning. Ph.D. thesis, Universit\u00e8 de Lieg\u00e8, Lieg\u00e8, Belgique (2022)"},{"key":"16_CR29","unstructured":"Sabatelli, M., et al.: Advances in digital music iconography: benchmarking the detection of musical instruments in unrestricted, non-photorealistic images from the artistic domain. Digital Humanit. Q. 15(1) (2021)"},{"key":"16_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1007\/978-3-030-11012-3_48","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"M Sabatelli","year":"2019","unstructured":"Sabatelli, M., Kestemont, M., Daelemans, W., Geurts, P.: Deep transfer learning for art classification problems. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11130, pp. 631\u2013646. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11012-3_48"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference On Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"16_CR32","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"16_CR33","unstructured":"Strezoski, G., Worring, M.: OmniArt: multi-task deep learning for artistic data analysis. arXiv preprint arXiv:1708.00684 (2017)"},{"key":"16_CR34","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.cogsys.2018.12.007","volume":"54","author":"M Talo","year":"2019","unstructured":"Talo, M., Baloglu, U.B., Y\u0131ld\u0131r\u0131m, \u00d6., Acharya, U.R.: Application of deep transfer learning for automated brain abnormality classification using MR images. Cogn. Syst. Res. 54, 176\u2013188 (2019)","journal-title":"Cogn. Syst. Res."},{"key":"16_CR35","unstructured":"Tan, M., Le, Q.: EfficientNetv2: smaller models and faster training. In: International Conference on Machine Learning, pp. 10096\u201310106. PMLR (2021)"},{"key":"16_CR36","doi-asserted-by":"crossref","unstructured":"Tan, W.R., Chan, C.S., Aguirre, H.E., Tanaka, K.: Ceci n\u2019est pas une pipe: a deep convolutional network for fine-art paintings classification. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3703\u20133707. IEEE (2016)","DOI":"10.1109\/ICIP.2016.7533051"},{"key":"16_CR37","unstructured":"Taylor, M.E., Stone, P.: Transfer learning for reinforcement learning domains: a survey. J. Mach. Learn. Res. 10(7), 1633\u20131685 (2009)"},{"key":"16_CR38","unstructured":"Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., J\u00e9gou, H.: Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning, vol. 139, pp. 10347\u201310357 (2021)"},{"key":"16_CR39","unstructured":"Van Den Oord, A., Dieleman, S., Schrauwen, B.: Transfer learning by supervised pre-training for audio-based music classification. In: Conference of the International Society for Music Information Retrieval (ISMIR 2014) (2014)"},{"issue":"8","key":"16_CR40","doi-asserted-by":"publisher","first-page":"4435","DOI":"10.5194\/hess-25-4435-2021","volume":"25","author":"R Vandaele","year":"2021","unstructured":"Vandaele, R., Dance, S.L., Ojha, V.: Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning. Hydrol. Earth Syst. Sci. 25(8), 4435\u20134453 (2021)","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"16_CR41","doi-asserted-by":"crossref","unstructured":"Xue, F., Wang, Q., Guo, G.: Transfer: learning relation-aware facial expression representations with transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3601\u20133610 (2021)","DOI":"10.1109\/ICCV48922.2021.00358"},{"key":"16_CR42","unstructured":"Ying, W., Zhang, Y., Huang, J., Yang, Q.: Transfer learning via learning to transfer. In: International Conference on Machine Learning, pp. 5085\u20135094. PMLR (2018)"},{"key":"16_CR43","doi-asserted-by":"crossref","unstructured":"Zhong, S.H., Huang, X., Xiao, Z.: Fine-art painting classification via two-channel dual path networks. Int. J. Mach. Learn. Cybern. 11(1), 137\u2013152 (2020)","DOI":"10.1007\/s13042-019-00963-0"},{"key":"16_CR44","doi-asserted-by":"crossref","unstructured":"Zhou, H.Y., Lu, C., Yang, S., Yu, Y.: Convnets vs. transformers: whose visual representations are more transferable? In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2230\u20132238 (2021)","DOI":"10.1109\/ICCVW54120.2021.00252"},{"key":"16_CR45","unstructured":"Zhu, Z., Lin, K., Zhou, J.: Transfer learning in deep reinforcement learning: a survey. arXiv preprint arXiv:2009.07888 (2020)"},{"issue":"1","key":"16_CR46","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2020","unstructured":"Zhuang, F., et al.: A comprehensive survey on transfer learning. Proc. IEEE 109(1), 43\u201376 (2020)","journal-title":"Proc. IEEE"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25056-9_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:30:35Z","timestamp":1710268235000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25056-9_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250552","9783031250569"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25056-9_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"15 February 2023","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","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":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1645","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.21","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.91","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"From the workshops, 367 reviewed full papers have been selected for publication","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}