{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T22:50:52Z","timestamp":1768690252111,"version":"3.49.0"},"publisher-location":"Cham","reference-count":62,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030012304","type":"print"},{"value":"9783030012311","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-01231-1_24","type":"book-chapter","created":{"date-parts":[[2018,10,5]],"date-time":"2018-10-05T20:03:25Z","timestamp":1538769805000},"page":"389-406","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["PM-GANs: Discriminative Representation Learning for Action Recognition Using Partial-Modalities"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7341-4904","authenticated-orcid":false,"given":"Lan","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenqiang","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luyu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wangmeng","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deyu","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,10,6]]},"reference":[{"issue":"3","key":"24_CR1","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/34.910878","volume":"23","author":"AF Bobick","year":"2001","unstructured":"Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. Mach. Intell. 23(3), 257\u2013267 (2001)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"24_CR2","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.cviu.2013.11.009","volume":"122","author":"T Bouwmans","year":"2014","unstructured":"Bouwmans, T., Zahzah, E.H.: Robust PCA via principal component pursuit: a review for a comparative evaluation in video surveillance. Comput. Vis. Image Underst. 122, 22\u201334 (2014)","journal-title":"Comput. Vis. Image Underst."},{"key":"24_CR3","doi-asserted-by":"crossref","unstructured":"Bronstein, M.M., Bronstein, A.M., Michel, F., Paragios, N.: Data fusion through cross-modality metric learning using similarity-sensitive hashing. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3594\u20133601. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5539928"},{"issue":"1","key":"24_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10346-016-0708-4","volume":"14","author":"DT Bui","year":"2017","unstructured":"Bui, D.T., Nguyen, Q.P., Hoang, N.D., Klempe, H.: A novel fuzzy k-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS. Landslides 14(1), 1\u201317 (2017)","journal-title":"Landslides"},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Carreira, J., Zisserman, A.: Quo vadis, action recognition? A new model and the kinetics dataset. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4724\u20134733. IEEE (2017)","DOI":"10.1109\/CVPR.2017.502"},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Castrejon, L., Aytar, Y., Vondrick, C., Pirsiavash, H., Torralba, A.: Learning aligned cross-modal representations from weakly aligned data. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2940\u20132949. IEEE (2016)","DOI":"10.1109\/CVPR.2016.321"},{"key":"24_CR7","doi-asserted-by":"crossref","unstructured":"Delhumeau, J., Gosselin, P.H., J\u00e9gou, H., P\u00e9rez, P.: Revisiting the VLAD image representation. In: Proceedings of the 21st ACM International Conference on Multimedia, pp. 653\u2013656. ACM (2013)","DOI":"10.1145\/2502081.2502171"},{"key":"24_CR8","unstructured":"Denton, E.L., Chintala, S., Fergus, R., et al.: Deep generative image models using a Laplacian pyramid of adversarial networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 1486\u20131494. MIT Press (2015)"},{"key":"24_CR9","doi-asserted-by":"crossref","unstructured":"Feichtenhofer, C., Pinz, A., Zisserman, A.: Convolutional two-stream network fusion for video action recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1933\u20131941. IEEE (2016)","DOI":"10.1109\/CVPR.2016.213"},{"key":"24_CR10","doi-asserted-by":"crossref","unstructured":"Feng, F., Wang, X., Li, R.: Cross-modal retrieval with correspondence autoencoder. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 7\u201316. ACM (2014)","DOI":"10.1145\/2647868.2654902"},{"issue":"4","key":"24_CR11","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1109\/TPAMI.2016.2558148","volume":"39","author":"B Fernando","year":"2017","unstructured":"Fernando, B., Gavves, E., Oramas, J., Ghodrati, A., Tuytelaars, T.: Rank pooling for action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 773\u2013787 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"24_CR12","unstructured":"Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning (ICML), pp. 1180\u20131189. ACM (2015)"},{"key":"24_CR13","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.neucom.2016.05.094","volume":"212","author":"C Gao","year":"2016","unstructured":"Gao, C., et al.: Infar dataset: infrared action recognition at different times. Neurocomputing 212, 36\u201347 (2016)","journal-title":"Neurocomputing"},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"van Gemert, J.C., Jain, M., Gati, E., Snoek, C.G., et al.: Apt: Action localization proposals from dense trajectories. In: British Machine Vision Conference (BMVC), pp. 177.1\u2013177.12. British Machine Vision Association (2015)","DOI":"10.5244\/C.29.177"},{"key":"24_CR15","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (NIPS), pp. 2672\u20132680. MIT Press (2014)"},{"key":"24_CR16","doi-asserted-by":"crossref","unstructured":"Gupta, S., Hoffman, J., Malik, J.: Cross modal distillation for supervision transfer. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2827\u20132836. IEEE (2016)","DOI":"10.1109\/CVPR.2016.309"},{"key":"24_CR17","unstructured":"Han, J., Bhanu, B.: Human activity recognition in thermal infrared imagery. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR Workshops), p. 17. IEEE (2005)"},{"issue":"6","key":"24_CR18","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1016\/j.patcog.2006.11.010","volume":"40","author":"J Han","year":"2007","unstructured":"Han, J., Bhanu, B.: Fusion of color and infrared video for moving human detection. Pattern Recogn. 40(6), 1771\u20131784 (2007)","journal-title":"Pattern Recogn."},{"issue":"12","key":"24_CR19","doi-asserted-by":"publisher","first-page":"2639","DOI":"10.1162\/0899766042321814","volume":"16","author":"DR Hardoon","year":"2004","unstructured":"Hardoon, D.R., Szedmak, S., Shawe-Taylor, J.: Canonical correlation analysis: an overview with application to learning methods. Neural Comput. 16(12), 2639\u20132664 (2004)","journal-title":"Neural Comput."},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778. IEEE (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"1","key":"24_CR21","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","volume":"35","author":"S Ji","year":"2012","unstructured":"Ji, S., Yang, M., Yu, K.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221\u2013231 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Rozgic, V., Adali, S.: Learning spatiotemporal features for infrared action recognition with 3D convolutional neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR Workshops). IEEE (2017)","DOI":"10.1109\/CVPRW.2017.44"},{"issue":"3","key":"24_CR23","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1109\/TMM.2015.2390499","volume":"17","author":"C Kang","year":"2015","unstructured":"Kang, C., Xiang, S., Liao, S., Xu, C., Pan, C.: Learning consistent feature representation for cross-modal multimedia retrieval. IEEE Trans. Multimed. 17(3), 370\u2013381 (2015)","journal-title":"IEEE Trans. Multimed."},{"key":"24_CR24","doi-asserted-by":"crossref","unstructured":"Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1725\u20131732. IEEE (2014)","DOI":"10.1109\/CVPR.2014.223"},{"key":"24_CR25","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"24_CR26","doi-asserted-by":"crossref","unstructured":"Klaser, A., Marsza\u0142ek, M., Schmid, C.: A spatio-temporal descriptor based on 3D-gradients. In: British Machine Vision Conference (BMVC), pp. 275-1\u201310. British Machine Vision Association (2008)","DOI":"10.5244\/C.22.99"},{"key":"24_CR27","unstructured":"Kumar, S., Udupa, R.: Learning hash functions for cross-view similarity search. In: International Koint Conference on Artificial Intelligence (IJCAI), pp. 1360\u20131365. AAAI Press (2011)"},{"issue":"2\u20133","key":"24_CR28","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s11263-005-1838-7","volume":"64","author":"I Laptev","year":"2005","unstructured":"Laptev, I.: On space-time interest points. Int. J. Comput. Vis. 64(2\u20133), 107\u2013123 (2005)","journal-title":"Int. J. Comput. Vis."},{"key":"24_CR29","doi-asserted-by":"crossref","unstructured":"Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1\u20138. IEEE (2008)","DOI":"10.1109\/CVPR.2008.4587756"},{"issue":"4","key":"24_CR30","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1109\/TCSVT.2010.2041828","volume":"21","author":"T Li","year":"2011","unstructured":"Li, T., Mei, T., Kweon, I.S., Hua, X.S.: Contextual bag-of-words for visual categorization. IEEE Trans. Circ. Syst. Video Technol. 21(4), 381\u2013392 (2011)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"24_CR31","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.cviu.2017.10.011","volume":"166","author":"Z Li","year":"2018","unstructured":"Li, Z., Gavrilyuk, K., Gavves, E., Jain, M., Snoek, C.G.: Videolstm convolves, attends and flows for action recognition. Comput. Vis. Image Underst. 166, 41\u201350 (2018)","journal-title":"Comput. Vis. Image Underst."},{"key":"24_CR32","doi-asserted-by":"crossref","unstructured":"Lindtner, S., Hertz, G.D., Dourish, P.: Emerging sites of HCI innovation: hackerspaces, hardware startups & incubators. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 439\u2013448. ACM (2014)","DOI":"10.1145\/2556288.2557132"},{"issue":"7","key":"24_CR33","doi-asserted-by":"publisher","first-page":"1781","DOI":"10.1109\/TCYB.2016.2582918","volume":"47","author":"AA Liu","year":"2017","unstructured":"Liu, A.A., Xu, N., Nie, W.Z., Su, Y.T., Wong, Y., Kankanhalli, M.: Benchmarking a multimodal and multiview and interactive dataset for human action recognition. IEEE Trans. Cybern. 47(7), 1781\u20131794 (2017)","journal-title":"IEEE Trans. Cybern."},{"key":"24_CR34","unstructured":"Liu, C., et al.: Beyond pixels: exploring new representations and applications for motion analysis. Ph.D. thesis, Massachusetts Institute of Technology (2009)"},{"key":"24_CR35","doi-asserted-by":"crossref","unstructured":"Liu, Y., Lu, Z., Li, J., Yao, C., Deng, Y.: Transferable feature representation for visible-to-infrared cross-dataset human action recognition. Complexity 2018, Article ID 5345241, 20 p. (2018)","DOI":"10.1155\/2018\/5345241"},{"key":"24_CR36","unstructured":"Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: International Conference on Machine Learning (ICML), pp. 97\u2013105. ACM (2015)"},{"issue":"3","key":"24_CR37","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/MSP.2014.2347059","volume":"32","author":"VM Patel","year":"2015","unstructured":"Patel, V.M., Gopalan, R., Li, R., Chellappa, R.: Visual domain adaptation: a survey of recent advances. IEEE Sig. Process. Mag. 32(3), 53\u201369 (2015)","journal-title":"IEEE Sig. Process. Mag."},{"key":"24_CR38","unstructured":"Peng, Y., Qi, J., Yuan, Y.: CM-GANs: cross-modal generative adversarial networks for common representation learning. arXiv preprint arXiv:1710.05106 (2017)"},{"issue":"3","key":"24_CR39","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1109\/TPAMI.2013.142","volume":"36","author":"JC Pereira","year":"2014","unstructured":"Pereira, J.C., et al.: On the role of correlation and abstraction in cross-modal multimedia retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 36(3), 521\u2013535 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"24_CR40","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/978-3-642-15561-1_11","volume-title":"Computer Vision \u2013 ECCV 2010","author":"F Perronnin","year":"2010","unstructured":"Perronnin, F., S\u00e1nchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 143\u2013156. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15561-1_11"},{"key":"24_CR41","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)"},{"issue":"3","key":"24_CR42","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1109\/TPAMI.2017.2691768","volume":"40","author":"H Rahmani","year":"2018","unstructured":"Rahmani, H., Mian, A., Shah, M.: Learning a deep model for human action recognition from novel viewpoints. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 667\u2013681 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"24_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-012-9356-9","volume":"43","author":"SS Rautaray","year":"2015","unstructured":"Rautaray, S.S., Agrawal, A.: Vision based hand gesture recognition for human computer interaction: a survey. Artif. Intell. Rev. 43(1), 1\u201354 (2015)","journal-title":"Artif. Intell. Rev."},{"issue":"3","key":"24_CR44","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1007\/s11263-013-0636-x","volume":"105","author":"J S\u00e1nchez","year":"2013","unstructured":"S\u00e1nchez, J., Perronnin, F., Mensink, T., Verbeek, J.: Image classification with the fisher vector: theory and practice. Int. J. Comput. Vis. 105(3), 222\u2013245 (2013)","journal-title":"Int. J. Comput. Vis."},{"key":"24_CR45","doi-asserted-by":"crossref","unstructured":"Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: 2004 International Conference on Pattern Recognition, (ICPR), vol. 3, pp. 32\u201336. IEEE (2004)","DOI":"10.1109\/ICPR.2004.1334462"},{"issue":"5","key":"24_CR46","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1109\/TNNLS.2014.2330900","volume":"26","author":"L Shao","year":"2015","unstructured":"Shao, L., Zhu, F., Li, X.: Transfer learning for visual categorization: a survey. IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1019\u20131034 (2015)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"5","key":"24_CR47","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"HC Shin","year":"2016","unstructured":"Shin, H.C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285\u20131298 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"24_CR48","unstructured":"Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Advances in Neural Information Processing Systems (NIPS), pp. 568\u2013576. MIT Press (2014)"},{"key":"24_CR49","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 4489\u20134497. IEEE (2015)","DOI":"10.1109\/ICCV.2015.510"},{"key":"24_CR50","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2962\u20132971. IEEE (2017)","DOI":"10.1109\/CVPR.2017.316"},{"key":"24_CR51","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.1109\/TPAMI.2017.2712608","volume":"40","author":"G Varol","year":"2018","unstructured":"Varol, G., Laptev, I., Schmid, C.: Long-term temporal convolutions for action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40, 1510\u20131517 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"24_CR52","doi-asserted-by":"crossref","unstructured":"Veeriah, V., Zhuang, N., Qi, G.J.: Differential recurrent neural networks for action recognition. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 4041\u20134049. IEEE (2015)","DOI":"10.1109\/ICCV.2015.460"},{"issue":"1","key":"24_CR53","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1007\/s11263-012-0594-8","volume":"103","author":"H Wang","year":"2013","unstructured":"Wang, H., Kl\u00e4ser, A., Schmid, C., Liu, C.L.: Dense trajectories and motion boundary descriptors for action recognition. Int. J. Comput. Vis. 103(1), 60\u201379 (2013)","journal-title":"Int. J. Comput. Vis."},{"issue":"3","key":"24_CR54","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/s11263-015-0846-5","volume":"119","author":"H Wang","year":"2016","unstructured":"Wang, H., Oneata, D., Verbeek, J., Schmid, C.: A robust and efficient video representation for action recognition. Int. J. Comput. Vis. 119(3), 219\u2013238 (2016)","journal-title":"Int. J. Comput. Vis."},{"key":"24_CR55","doi-asserted-by":"crossref","unstructured":"Wang, L., Qiao, Y., Tang, X.: Action recognition with trajectory-pooled deep-convolutional descriptors. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4305\u20134314. IEEE (2015)","DOI":"10.1109\/CVPR.2015.7299059"},{"key":"24_CR56","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/978-3-319-46484-8_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"L Wang","year":"2016","unstructured":"Wang, L., et al.: Temporal segment networks: towards good practices for deep action recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 20\u201336. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_2"},{"issue":"2","key":"24_CR57","first-page":"449","volume":"47","author":"Y Wei","year":"2017","unstructured":"Wei, Y., et al.: Cross-modal retrieval with CNN visual features: a new baseline. IEEE Trans. Cybern. 47(2), 449\u2013460 (2017)","journal-title":"IEEE Trans. Cybern."},{"key":"24_CR58","doi-asserted-by":"crossref","unstructured":"Wu, A., Zheng, W.S., Yu, H.X., Gong, S., Lai, J.: RGB-infrared cross-modality person re-identification. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5380\u20135389. IEEE (2017)","DOI":"10.1109\/ICCV.2017.575"},{"key":"24_CR59","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1007\/978-3-319-16814-2_33","volume-title":"Computer Vision \u2013 ACCV 2014","author":"L Yang","year":"2015","unstructured":"Yang, L., Gao, C., Meng, D., Jiang, L.: A novel group-sparsity-optimization-based feature selection model for complex interaction recognition. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9007, pp. 508\u2013521. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-16814-2_33"},{"issue":"6","key":"24_CR60","doi-asserted-by":"publisher","first-page":"1677","DOI":"10.1109\/TMM.2014.2323014","volume":"16","author":"Y Yang","year":"2014","unstructured":"Yang, Y., Zha, Z.J., Gao, Y., Zhu, X., Chua, T.S.: Exploiting web images for semantic video indexing via robust sample-specific loss. IEEE Trans. Multimed. 16(6), 1677\u20131689 (2014)","journal-title":"IEEE Trans. Multimed."},{"issue":"5","key":"24_CR61","doi-asserted-by":"publisher","first-page":"2009","DOI":"10.1109\/TIP.2014.2310992","volume":"23","author":"YR Yeh","year":"2014","unstructured":"Yeh, Y.R., Huang, C.H., Wang, Y.C.F.: Heterogeneous domain adaptation and classification by exploiting the correlation subspace. IEEE Trans. Image Process. 23(5), 2009\u20132018 (2014)","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"24_CR62","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1145\/2601097.2601165","volume":"33","author":"M Zollh\u00f6fer","year":"2014","unstructured":"Zollh\u00f6fer, M., et al.: Real-time non-rigid reconstruction using an RGB-D camera. ACM Trans. Graph. (TOG) 33(4), 156 (2014)","journal-title":"ACM Trans. Graph. (TOG)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2018"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01231-1_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T00:10:16Z","timestamp":1664928616000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-01231-1_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030012304","9783030012311"],"references-count":62,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01231-1_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"6 October 2018","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":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2018.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"}]}}