{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T00:31:37Z","timestamp":1781915497068,"version":"3.54.5"},"publisher-location":"Cham","reference-count":62,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030695347","type":"print"},{"value":"9783030695354","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-69535-4_1","type":"book-chapter","created":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T15:13:11Z","timestamp":1614179591000},"page":"3-20","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":118,"title":["End-to-End Model-Based Gait Recognition"],"prefix":"10.1007","author":[{"given":"Xiang","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasushi","family":"Makihara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chi","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasushi","family":"Yagi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiqi","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingwu","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,2,25]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1111\/j.1556-4029.2011.01793.x","volume":"56","author":"I Bouchrika","year":"2011","unstructured":"Bouchrika, I., Goffredo, M., Carter, J., Nixon, M.: On using gait in forensic biometrics. J. Forensic Sci. 56, 882\u2013889 (2011)","journal-title":"J. Forensic Sci."},{"key":"1_CR2","doi-asserted-by":"publisher","first-page":"163","DOI":"10.2197\/ipsjtcva.5.163","volume":"5","author":"H Iwama","year":"2013","unstructured":"Iwama, H., Muramatsu, D., Makihara, Y., Yagi, Y.: Gait verification system for criminal investigation. IPSJ Trans. Comput. Vis. Appl. 5, 163\u2013175 (2013)","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"1_CR3","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1049\/iet-bmt.2013.0090","volume":"3","author":"N Lynnerup","year":"2014","unstructured":"Lynnerup, N., Larsen, P.: Gait as evidence. IET Biometrics 3, 47\u201354 (2014)","journal-title":"IET Biometrics"},{"key":"1_CR4","unstructured":"Wagg, D., Nixon, M.: On automated model-based extraction and analysis of gait. In: Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 11\u201316 (2004)"},{"key":"1_CR5","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1016\/j.patcog.2003.09.012","volume":"37","author":"C Yam","year":"2004","unstructured":"Yam, C., Nixon, M., Carter, J.: Automated person recognition by walking and running via model-based approaches. Pattern Recogn. 37, 1057\u20131072 (2004)","journal-title":"Pattern Recogn."},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Bobick, A., Johnson, A.: Gait recognition using static activity-specific parameters. In: CVPR, vol. 1, pp. 423\u2013430 (2001)","DOI":"10.1109\/CVPR.2001.990506"},{"key":"1_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S1077-3142(03)00008-0","volume":"90","author":"D Cunado","year":"2003","unstructured":"Cunado, D., Nixon, M., Carter, J.: Automatic extraction and description of human gait models for recognition purposes. Comput. Vis. Image Underst. 90, 1\u201341 (2003)","journal-title":"Comput. Vis. Image Underst."},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Yamauchi, K., Bhanu, B., Saito, H.: 3D human body modeling using range data. In: ICPR, pp. 3476\u20133479 (2010)","DOI":"10.1109\/ICPR.2010.849"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Ariyanto, G., Nixon, M.: Marionette mass-spring model for 3d gait biometrics. In: Proceedings of the 5th IAPR International Conference on Biometrics, pp. 354\u2013359 (2012)","DOI":"10.1109\/ICB.2012.6199832"},{"key":"1_CR10","unstructured":"Feng, Y., Li, Y., Luo, J.: Learning effective gait features using LSTM. In: ICPR, pp. 325\u2013330 (2016)"},{"key":"1_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1007\/978-3-319-69923-3_51","volume-title":"Biometric Recognition","author":"R Liao","year":"2017","unstructured":"Liao, R., Cao, C., Garcia, E.B., Yu, S., Huang, Y.: Pose-based temporal-spatial network (PTSN) for gait recognition with carrying and clothing variations. In: Zhou, J., et al. (eds.) CCBR 2017. LNCS, vol. 10568, pp. 474\u2013483. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-69923-3_51"},{"key":"1_CR12","doi-asserted-by":"publisher","first-page":"107069","DOI":"10.1016\/j.patcog.2019.107069","volume":"98","author":"R Liao","year":"2020","unstructured":"Liao, R., Yu, S., An, W., Huang, Y.: A model-based gait recognition method with body pose and human prior knowledge. Pattern Recogn. 98, 107069 (2020)","journal-title":"Pattern Recogn."},{"key":"1_CR13","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1109\/TPAMI.2006.38","volume":"28","author":"J Han","year":"2006","unstructured":"Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell. 28, 316\u2013322 (2006)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1_CR14","unstructured":"Xu, D., Yan, S., Tao, D., Zhang, L., Li, X., Zhang, H.: Human gait recognition with matrix representation. IEEE Trans. Circuits Syst. Video Technol. 16, 896\u2013903 (2006)"},{"key":"1_CR15","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.patrec.2009.11.006","volume":"31","author":"J Lu","year":"2010","unstructured":"Lu, J., Tan, Y.P.: Uncorrelated discriminant simplex analysis for view-invariant gait signal computing. Pattern Recogn. Lett. 31, 382\u2013393 (2010)","journal-title":"Pattern Recogn. Lett."},{"key":"1_CR16","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.1109\/TPAMI.2014.2366766","volume":"37","author":"Y Guan","year":"2015","unstructured":"Guan, Y., Li, C.T., Roli, F.: On reducing the effect of covariate factors in gait recognition: a classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 37, 1521\u20131528 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"Makihara, Y., Suzuki, A., Muramatsu, D., Li, X., Yagi, Y.: Joint intensity and spatial metric learning for robust gait recognition. In: CVPR, pp. 5705\u20135715 (2017)","DOI":"10.1109\/CVPR.2017.718"},{"key":"1_CR18","doi-asserted-by":"crossref","unstructured":"Shiraga, K., Makihara, Y., Muramatsu, D., Echigo, T., Yagi, Y.: Geinet: View-invariant gait recognition using a convolutional neural network. In: ICB (2016)","DOI":"10.1109\/ICB.2016.7550060"},{"key":"1_CR19","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1109\/TPAMI.2016.2545669","volume":"39","author":"Z Wu","year":"2017","unstructured":"Wu, Z., Huang, Y., Wang, L., Wang, X., Tan, T.: A comprehensive study on cross-view gait based human identification with deep CNNs. IEEE Trans. Pattern Anal. Mach. Intell. 39, 209\u2013226 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1_CR20","doi-asserted-by":"publisher","first-page":"2708","DOI":"10.1109\/TCSVT.2017.2760835","volume":"29","author":"N Takemura","year":"2019","unstructured":"Takemura, N., Makihara, Y., Muramatsu, D., Echigo, T., Yagi, Y.: On input\/output architectures for convolutional neural network-based cross-view gait recognition. IEEE Trans. Circuits Syst. Video Technol. 29, 2708\u20132719 (2019)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, K., Luo, W., Ma, L., Liu, W., Li, H.: Learning joint gait representation via quintuplet loss minimization. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00483"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Chao, H., He, Y., Zhang, J., Feng, J.: Gaitset: regarding gait as a set for cross-view gait recognition. In: AAAI (2019)","DOI":"10.1609\/aaai.v33i01.33018126"},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Li, X., Makihara, Y., Xu, C., Yagi, Y., Ren, M.: Joint intensity transformer network for gait recognition robust against clothing and carrying status. IEEE Trans. Inf. Forensics Secur. 1 (2019)","DOI":"10.1109\/TIFS.2019.2912577"},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Kusakunniran, W., Wu, Q., Zhang, J., Li, H.: Support vector regression for multi-view gait recognition based on local motion feature selection. In: CVPR, San Francisco, CA, USA, pp. 1\u20138 (2010)","DOI":"10.1109\/CVPR.2010.5540113"},{"key":"1_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/11744078_12","volume-title":"Computer Vision \u2013 ECCV 2006","author":"Y Makihara","year":"2006","unstructured":"Makihara, Y., Sagawa, R., Mukaigawa, Y., Echigo, T., Yagi, Y.: Gait recognition using a view transformation model in the frequency domain. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 151\u2013163. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11744078_12"},{"key":"1_CR26","unstructured":"Makihara, Y., Tsuji, A., Yagi, Y.: Silhouette transformation based on walking speed for gait identification. In: CVPR, San Francisco, CA, USA (2010)"},{"key":"1_CR27","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1109\/TIP.2014.2371335","volume":"24","author":"D Muramatsu","year":"2015","unstructured":"Muramatsu, D., Shiraishi, A., Makihara, Y., Uddin, M., Yagi, Y.: Gait-based person recognition using arbitrary view transformation model. IEEE Trans. Image Process. 24, 140\u2013154 (2015)","journal-title":"IEEE Trans. Image Process."},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"Mansur, A., Makihara, Y., Aqmar, R., Yagi, Y.: Gait recognition under speed transition. In: CVPR, pp. 2521\u20132528 (2014)","DOI":"10.1109\/CVPR.2014.323"},{"key":"1_CR29","doi-asserted-by":"crossref","unstructured":"Akae, N., Mansur, A., Makihara, Y., Yagi, Y.: Video from nearly still: an application to low frame-rate gait recognition. In: CVPR, Providence, RI, USA, pp. 1537\u20131543 (2012)","DOI":"10.1109\/CVPR.2012.6247844"},{"key":"1_CR30","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.patcog.2018.10.019","volume":"87","author":"S Yu","year":"2019","unstructured":"Yu, S., et al.: GaiTGANv 2: invariant gait feature extraction using generative adversarial networks. Pattern Recogn. 87, 179\u2013189 (2019)","journal-title":"Pattern Recogn."},{"key":"1_CR31","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1109\/TIFS.2018.2844819","volume":"14","author":"Y He","year":"2019","unstructured":"He, Y., Zhang, J., Shan, H., Wang, L.: Multi-task GANs for view-specific feature learning in gait recognition. IEEE Trans. Inf. Forensics Secur. 14, 102\u2013113 (2019)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"1_CR32","unstructured":"Wang, C., Zhang, J., Wang, L., Pu, J., Yuan, X.: Human identification using temporal information preserving gait template. IEEE Trans. Pattern Anal. Mach. Intell. 34, 2164\u20132176 (2012)"},{"key":"1_CR33","doi-asserted-by":"crossref","unstructured":"Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., Sheikh, Y.: OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields. arXiv preprint arXiv:1812.08008 (2018)","DOI":"10.1109\/CVPR.2017.143"},{"key":"1_CR34","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., Reid, I.D.: RefineNet: multi-path refinement networks for high-resolution semantic segmentation. In: CVPR, pp. 5168\u20135177 (2017)","DOI":"10.1109\/CVPR.2017.549"},{"key":"1_CR35","doi-asserted-by":"publisher","first-page":"106988","DOI":"10.1016\/j.patcog.2019.106988","volume":"96","author":"C Song","year":"2019","unstructured":"Song, C., Huang, Y., Huang, Y., Jia, N., Wang, L.: GaitNet: an end-to-end network for gait based human identification. Pattern Recogn. 96, 106988 (2019)","journal-title":"Pattern Recogn."},{"key":"1_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, Z., et al.: Gait recognition via disentangled representation learning. In: CVPR, Long Beach, CA (2019)","DOI":"10.1109\/CVPR.2019.00484"},{"key":"1_CR37","doi-asserted-by":"crossref","unstructured":"Kanazawa, A., Black, M.J., Jacobs, D.W., Malik, J.: End-to-end recovery of human shape and pose. In: CVPR, pp. 7122\u20137131 (2018)","DOI":"10.1109\/CVPR.2018.00744"},{"key":"1_CR38","doi-asserted-by":"crossref","unstructured":"Pavlakos, G., Zhu, L., Zhou, X., Daniilidis, K.: Learning to estimate 3D human pose and shape from a single color image. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00055"},{"key":"1_CR39","doi-asserted-by":"crossref","unstructured":"Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multi-person linear model. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 34, 248:1\u2013248:16 (2015)","DOI":"10.1145\/2816795.2818013"},{"key":"1_CR40","unstructured":"Yu, S., Tan, D., Tan, T.: A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: ICPR, Hong Kong, China, vol. 4, pp. 441\u2013444 (2006)"},{"key":"1_CR41","doi-asserted-by":"crossref","unstructured":"Pfister, T., Charles, J., Zisserman, A.: Flowing convnets for human pose estimation in videos. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.222"},{"key":"1_CR42","doi-asserted-by":"crossref","unstructured":"Fan, C., et al.: Gaitpart: temporal part-based model for gait recognition. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01423"},{"key":"1_CR43","doi-asserted-by":"crossref","unstructured":"Tran, L., Yin, X., Liu, X.: Disentangled representation learning GAN for pose-invariant face recognition. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.141"},{"key":"1_CR44","doi-asserted-by":"crossref","unstructured":"Esser, P., Sutter, E., Ommer, B.: A variational U-net for conditional appearance and shape generation. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00923"},{"key":"1_CR45","doi-asserted-by":"crossref","unstructured":"Li, X., Makihara, Y., Xu, C., Yagi, Y., Ren, M.: Gait recognition via semi-supervised disentangled representation learning to identity and covariate features. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01332"},{"key":"1_CR46","doi-asserted-by":"crossref","unstructured":"Liu, W., Piao, Z., Min, J., Luo, W., Ma, L., Gao, S.: Liquid warping GAN: a unified framework for human motion imitation, appearance transfer and novel view synthesis. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00600"},{"key":"1_CR47","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630\u2013645. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38"},{"key":"1_CR48","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"1_CR49","doi-asserted-by":"crossref","unstructured":"Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 2D human pose estimation: New benchmark and state of the art analysis. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.471"},{"key":"1_CR50","doi-asserted-by":"crossref","unstructured":"Johnson, S., Everingham, M.: Learning effective human pose estimation from inaccurate annotation. In: CVPR (2011)","DOI":"10.1109\/CVPR.2011.5995318"},{"key":"1_CR51","doi-asserted-by":"crossref","unstructured":"Johnson, S., Everingham, M.: Clustered pose and nonlinear appearance models for human pose estimation. In: BMVC (2010)","DOI":"10.5244\/C.24.12"},{"key":"1_CR52","doi-asserted-by":"crossref","unstructured":"Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3.6m: large scale datasets and predictive methods for 3D human sensing in natural environments. IEEE Trans. Pattern Anal. Mach. Intell. 36, 1325\u20131339 (2014)","DOI":"10.1109\/TPAMI.2013.248"},{"key":"1_CR53","doi-asserted-by":"crossref","unstructured":"Mehta, D., et al.: Monocular 3D human pose estimation in the wild using improved CNN supervision. In: Fifth International Conference on 3D Vision (3DV) (2017)","DOI":"10.1109\/3DV.2017.00064"},{"key":"1_CR54","doi-asserted-by":"crossref","unstructured":"Hiroharu Kato, Y.U., Harada, T.: Neural 3D mesh renderer. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00411"},{"key":"1_CR55","doi-asserted-by":"crossref","unstructured":"Wang, J., et al.: Learning fine-grained image similarity with deep ranking. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.180"},{"key":"1_CR56","doi-asserted-by":"crossref","unstructured":"Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: CVPR, vol. 2, pp. 1735\u20131742 (2006)","DOI":"10.1109\/CVPR.2006.100"},{"key":"1_CR57","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.B.: Mask R-CNN. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"1_CR58","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint (2014)"},{"key":"1_CR59","doi-asserted-by":"crossref","unstructured":"Fang, H.S., Xie, S., Tai, Y.W., Lu, C.: RMPE: regional multi-person pose estimation. In: The IEEE International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.256"},{"key":"1_CR60","unstructured":"Otsu, N.: Optimal linear and nonlinear solutions for least-square discriminant feature extraction. In: ICPR, pp. 557\u2013560 (1982)"},{"key":"1_CR61","doi-asserted-by":"crossref","unstructured":"Xu, C., Makihara, Y., Li, X., Yagi, Y., Lu, J.: Cross-view gait recognition using pairwise spatial transformer networks. IEEE Trans. Circuits Syst. Video Technol. 1 (2020)","DOI":"10.1109\/TCSVT.2020.2975671"},{"key":"1_CR62","doi-asserted-by":"publisher","first-page":"2034","DOI":"10.1109\/TIFS.2013.2287605","volume":"8","author":"M Hu","year":"2013","unstructured":"Hu, M., Wang, Y., Zhang, Z., Little, J.J., Huang, D.: View-invariant discriminative projection for multi-view gait-based human identification. IEEE Trans. Inf. Forensics Secur. 8, 2034\u20132045 (2013)","journal-title":"IEEE Trans. Inf. Forensics Secur."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2020"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-69535-4_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T20:01:24Z","timestamp":1724529684000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-69535-4_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030695347","9783030695354"],"references-count":62,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-69535-4_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"25 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2020","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":"accv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/accv2020.kyoto\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"768","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":"254","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":"33% - 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","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","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":"The conference was held virtually.","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)"}}]}}