{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T04:15:51Z","timestamp":1749701751963,"version":"3.41.0"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319488806"},{"type":"electronic","value":"9783319488813"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-48881-3_48","type":"book-chapter","created":{"date-parts":[[2016,11,2]],"date-time":"2016-11-02T13:19:08Z","timestamp":1478092748000},"page":"676-691","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Unsupervised Deep Domain Adaptation for Pedestrian Detection"],"prefix":"10.1007","author":[{"given":"Lihang","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiyao","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lisheng","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael Ying","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,11,3]]},"reference":[{"key":"48_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-642-15561-1_16","volume-title":"Computer Vision \u2013 ECCV 2010","author":"K Saenko","year":"2010","unstructured":"Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 213\u2013226. Springer, Heidelberg (2010)"},{"key":"48_CR2","doi-asserted-by":"crossref","unstructured":"Kulis, B., Saenko, K., Darrell, T.: What you saw is not what you get: domain adaptation using asymmetric kernel transforms. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1785\u20131792 (2011)","DOI":"10.1109\/CVPR.2011.5995702"},{"key":"48_CR3","doi-asserted-by":"crossref","unstructured":"Gopalan, R., Li, R., Chellappa, R.: Domain adaptation for object recognition: an unsupervised approach. In: IEEE International Conference on Computer Vision (ICCV), pp. 999\u20131006 (2011)","DOI":"10.1109\/ICCV.2011.6126344"},{"key":"48_CR4","doi-asserted-by":"crossref","unstructured":"Huang, J., Gretton, A., Borgwardt, K.M., Sch\u00f6lkopf, B., Smola, A.J.: Correcting sample selection bias by unlabeled data. In: Advances in Neural Information Processing Systems (NIPS), pp. 601\u2013608 (2006)","DOI":"10.7551\/mitpress\/7503.003.0080"},{"issue":"4","key":"48_CR5","first-page":"5","volume":"3","author":"A Gretton","year":"2009","unstructured":"Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., Sch\u00f6lkopf, B.: Covariate shift by kernel mean matching. Dataset Shift Mach. Learn. 3(4), 5 (2009)","journal-title":"Dataset Shift Mach. Learn."},{"issue":"2","key":"48_CR6","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1109\/TPAMI.2013.124","volume":"36","author":"X Wang","year":"2014","unstructured":"Wang, X., Wang, M., Li, W.: Scene-specific pedestrian detection for static video surveillance. IEEE Trans. Pattern Anal. Mach. Intell. 36(2), 361\u2013374 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"48_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1007\/978-3-319-10578-9_31","volume-title":"Computer Vision \u2013 ECCV 2014","author":"X Zeng","year":"2014","unstructured":"Zeng, X., Ouyang, W., Wang, M., Wang, X.: Deep learning of scene-specific classifier for pedestrian detection. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part III. LNCS, vol. 8691, pp. 472\u2013487. Springer, Heidelberg (2014)"},{"key":"48_CR8","doi-asserted-by":"crossref","unstructured":"Hattori, H., Naresh Boddeti, V., Kitani, K.M., Kanade, T.: Learning scene-specific pedestrian detectors without real data. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3819\u20133827 (2015)","DOI":"10.1109\/CVPR.2015.7299006"},{"key":"48_CR9","unstructured":"Mesnil, G., Dauphin, Y., Glorot, X., Rifai, S., Bengio, Y., Goodfellow, I.J., Lavoie, E., Muller, X., Desjardins, G., Warde-Farley, D., et al.: Unsupervised and transfer learning challenge: a deep learning approach. In: ICML Unsupervised and Transfer Learning Workshop, vol. 27, pp. 97\u2013110 (2012)"},{"key":"48_CR10","unstructured":"Gong, B., Grauman, K., Sha, F.: Connecting the dots with landmarks: discriminatively learning domain-invariant features for unsupervised domain adaptation. In: International Conference on Machine Learning (ICML), pp. 222\u2013230 (2013)"},{"key":"48_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1007\/978-3-319-13560-1_76","volume-title":"PRICAI 2014: Trends in Artificial Intelligence","author":"M Ghifary","year":"2014","unstructured":"Ghifary, M., Kleijn, W.B., Zhang, M.: Domain adaptive neural networks for object recognition. In: Pham, D.-N., Park, S.-B. (eds.) PRICAI 2014. LNCS, vol. 8862, pp. 898\u2013904. Springer, Heidelberg (2014)"},{"key":"48_CR12","doi-asserted-by":"crossref","unstructured":"Gretton, A., Borgwardt, K.M., Rasch, M., Sch\u00f6lkopf, B., Smola, A.J.: A kernel method for the two-sample-problem. In: Advances in Neural Information Processing Systems, pp. 513\u2013520 (2006)","DOI":"10.7551\/mitpress\/7503.003.0069"},{"key":"48_CR13","unstructured":"Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., Darrell, T.: Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474 (2014)"},{"key":"48_CR14","doi-asserted-by":"crossref","unstructured":"Pishchulin, L., Jain, A., Wojek, C., Andriluka, M., Thorm\u00e4hlen, T., Schiele, B.: Learning people detection models from few training samples. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1473\u20131480 (2011)","DOI":"10.1109\/CVPR.2011.5995574"},{"key":"48_CR15","doi-asserted-by":"crossref","unstructured":"Stewart, R., Andriluka, M., Ng, A.: End to end people detection in crowded scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.255"},{"issue":"1","key":"48_CR16","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"M Everingham","year":"2015","unstructured":"Everingham, M., Eslami, S.A., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. Int. J. Comput. Vision 111(1), 98\u2013136 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"48_CR17","unstructured":"Powers, D.M.: Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation (2011)"},{"key":"48_CR18","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 1097\u20131105 (2012)"},{"key":"48_CR19","doi-asserted-by":"crossref","unstructured":"Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2066\u20132073 (2012)","DOI":"10.1109\/CVPR.2012.6247911"},{"key":"48_CR20","doi-asserted-by":"crossref","unstructured":"Fernando, B., Habrard, A., Sebban, M., Tuytelaars, T.: Unsupervised visual domain adaptation using subspace alignment. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2960\u20132967 (2013)","DOI":"10.1109\/ICCV.2013.368"},{"key":"48_CR21","doi-asserted-by":"crossref","unstructured":"Tommasi, T., Caputo, B.: Frustratingly easy nbnn domain adaptation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 897\u2013904 (2013)","DOI":"10.1109\/ICCV.2013.116"},{"key":"48_CR22","unstructured":"Chopra, S., Balakrishnan, S., Gopalan, R.: Dlid: deep learning for domain adaptation by interpolating between domains. In: ICML Workshop on Challenges in Representation Learning, vol. 2 (2013)"},{"key":"48_CR23","unstructured":"Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: Decaf: A deep convolutional activation feature for generic visual recognition. arXiv preprint arXiv:1310.1531 (2013)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2016 Workshops"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-48881-3_48","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T23:24:10Z","timestamp":1749684250000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-48881-3_48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319488806","9783319488813"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-48881-3_48","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2016]]},"assertion":[{"value":"3 November 2016","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":"Amsterdam","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2016","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2016","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2016","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2016","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.eccv2016.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}