{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T20:27:38Z","timestamp":1782937658051,"version":"3.54.5"},"publisher-location":"Cham","reference-count":60,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031197802","type":"print"},{"value":"9783031197819","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-19781-9_27","type":"book-chapter","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T12:12:59Z","timestamp":1666440779000},"page":"462-479","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["Modality Synergy Complement Learning with Cascaded Aggregation for Visible-Infrared Person Re-Identification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6643-9698","authenticated-orcid":false,"given":"Yiyuan","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9386-9677","authenticated-orcid":false,"given":"Sanyuan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7506-352X","authenticated-orcid":false,"given":"Yuhao","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1883-2086","authenticated-orcid":false,"given":"Jianbing","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,23]]},"reference":[{"key":"27_CR1","doi-asserted-by":"crossref","unstructured":"Ahmed, S.M., Lejbolle, A.R., Panda, R., Roy-Chowdhury, A.K.: Camera on-boarding for person re-identification using hypothesis transfer learning. In: CVPR, pp. 12144\u201312153 (2020)","DOI":"10.1109\/CVPR42600.2020.01216"},{"key":"27_CR2","doi-asserted-by":"crossref","unstructured":"Bai, S., Tang, P., Torr, P.H., Latecki, L.J.: Re-ranking via metric fusion for object retrieval and person re-identification. In: CVPR, pp. 740\u2013749 (2019)","DOI":"10.1109\/CVPR.2019.00083"},{"key":"27_CR3","doi-asserted-by":"crossref","unstructured":"Chen, G., Lin, C., Ren, L., Lu, J., Zhou, J.: Self-critical attention learning for person re-identification. In: ICCV, pp. 9637\u20139646 (2019)","DOI":"10.1109\/ICCV.2019.00973"},{"key":"27_CR4","doi-asserted-by":"crossref","unstructured":"Chen, T., et al.: ABD-net: attentive but diverse person re-identification. In: CVPR, pp. 8351\u20138361 (2019)","DOI":"10.1109\/ICCV.2019.00844"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wan, L., Li, Z., Jing, Q., Sun, Z.: Neural feature search for RGB-infrared person re-identification. In: CVPR, pp. 587\u2013597, June 2021","DOI":"10.1109\/CVPR46437.2021.00065"},{"key":"27_CR6","doi-asserted-by":"crossref","unstructured":"Choi, S., Lee, S., Kim, Y., Kim, T., Kim, C.: Hi-CMD: hierarchical cross-modality disentanglement for visible-infrared person re-identification. In: CVPR, pp. 10257\u201310266 (2020)","DOI":"10.1109\/CVPR42600.2020.01027"},{"key":"27_CR7","doi-asserted-by":"crossref","unstructured":"Dai, P., Ji, R., Wang, H., Wu, Q., Huang, Y.: Cross-modality person re-identification with generative adversarial training. In: IJCAI, pp. 677\u2013683 (2018)","DOI":"10.24963\/ijcai.2018\/94"},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"Deng, W., Zheng, L., Ye, Q., Kang, G., Yang, Y., Jiao, J.: Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. In: CVPR, pp. 994\u20131003 (2018)","DOI":"10.1109\/CVPR.2018.00110"},{"key":"27_CR9","first-page":"579","volume":"29","author":"Z Feng","year":"2019","unstructured":"Feng, Z., Lai, J., Xie, X.: Learning modality-specific representations for visible-infrared person re-identification. IEEE TIP 29, 579\u2013590 (2019)","journal-title":"IEEE TIP"},{"key":"27_CR10","doi-asserted-by":"crossref","unstructured":"Fu, C., Hu, Y., Wu, X., Shi, H., Mei, T., He, R.: CM-NAS: cross-modality neural architecture search for visible-infrared person re-identification. In: ICCV, pp. 11823\u201311832, October 2021","DOI":"10.1109\/ICCV48922.2021.01161"},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Hao, X., Zhao, S., Ye, M., Shen, J.: Cross-modality person re-identification via modality confusion and center aggregation. In: ICCV, pp. 16403\u201316412, October 2021","DOI":"10.1109\/ICCV48922.2021.01609"},{"key":"27_CR12","doi-asserted-by":"crossref","unstructured":"Hao, Y., Wang, N., Li, J., Gao, X.: HSME: hypersphere manifold embedding for visible thermal person re-identification. In: AAAI, pp. 8385\u20138392 (2019)","DOI":"10.1609\/aaai.v33i01.33018385"},{"key":"27_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"27_CR14","unstructured":"Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)"},{"key":"27_CR15","doi-asserted-by":"crossref","unstructured":"Jia, M., Zhai, Y., Lu, S., Ma, S., Zhang, J.: A similarity inference metric for RGB-infrared cross-modality person re-identification. arXiv preprint arXiv:2007.01504 (2020)","DOI":"10.24963\/ijcai.2020\/143"},{"key":"27_CR16","doi-asserted-by":"crossref","unstructured":"Jin, X., Lan, C., Zeng, W., Chen, Z., Zhang, L.: Style normalization and restitution for generalizable person re-identification. In: CVPR, pp. 3143\u20133152 (2020)","DOI":"10.1109\/CVPR42600.2020.00321"},{"key":"27_CR17","doi-asserted-by":"crossref","unstructured":"Li, D., Wei, X., Hong, X., Gong, Y.: Infrared-visible cross-modal person re-identification with an x modality. In: AAAI, pp. 4610\u20134617 (2020)","DOI":"10.1609\/aaai.v34i04.5891"},{"key":"27_CR18","doi-asserted-by":"crossref","unstructured":"Li, H., Wu, G., Zheng, W.S.: Combined depth space based architecture search for person re-identification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6729\u20136738 (2021)","DOI":"10.1109\/CVPR46437.2021.00666"},{"key":"27_CR19","doi-asserted-by":"crossref","unstructured":"Li, Y., He, J., Zhang, T., Liu, X., Zhang, Y., Wu, F.: Diverse part discovery: occluded person re-identification with part-aware transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2898\u20132907 (2021)","DOI":"10.1109\/CVPR46437.2021.00292"},{"key":"27_CR20","doi-asserted-by":"crossref","unstructured":"Lin, Y., Xie, L., Wu, Y., Yan, C., Tian, Q.: Unsupervised person re-identification via softened similarity learning. In: CVPR, pp. 3390\u20133399 (2020)","DOI":"10.1109\/CVPR42600.2020.00345"},{"key":"27_CR21","doi-asserted-by":"crossref","unstructured":"Lu, Y., et al.: Cross-modality person re-identification with shared-specific feature transfer. In: CVPR, pp. 13379\u201313389 (2020)","DOI":"10.1109\/CVPR42600.2020.01339"},{"key":"27_CR22","doi-asserted-by":"crossref","unstructured":"Luo, C., Chen, Y., Wang, N., Zhang, Z.: Spectral feature transformation for person re-identification. In: CVPR, pp. 4976\u20134985 (2019)","DOI":"10.1109\/ICCV.2019.00508"},{"key":"27_CR23","doi-asserted-by":"crossref","unstructured":"Luo, H., Gu, Y., Liao, X., Lai, S., Jiang, W.: Bag of tricks and a strong baseline for deep person re-identification. In: CVPR Workshops (2019)","DOI":"10.1109\/CVPRW.2019.00190"},{"key":"27_CR24","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Lear. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Lear. Res."},{"key":"27_CR25","unstructured":"Melis, G., Ko\u010disk\u1ef3, T., Blunsom, P.: Mogrifier LSTM. arXiv preprint arXiv:1909.01792 (2019)"},{"issue":"10","key":"27_CR26","doi-asserted-by":"crossref","first-page":"6074","DOI":"10.1109\/TPAMI.2021.3084613","volume":"44","author":"J Meng","year":"2021","unstructured":"Meng, J., Zheng, W.S., Lai, J.H., Wang, L.: Deep graph metric learning for weakly supervised person re-identification. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6074\u20136093 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"27_CR27","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1068\/p2896","volume":"30","author":"H Moon","year":"2001","unstructured":"Moon, H., Phillips, P.J.: Computational and performance aspects of PCA-based face-recognition algorithms. Perception 30(3), 303\u2013321 (2001)","journal-title":"Perception"},{"issue":"3","key":"27_CR28","doi-asserted-by":"publisher","first-page":"605","DOI":"10.3390\/s17030605","volume":"17","author":"DT Nguyen","year":"2017","unstructured":"Nguyen, D.T., Hong, H.G., Kim, K.W., Park, K.R.: Person recognition system based on a combination of body images from visible light and thermal cameras. Sensors 17(3), 605 (2017)","journal-title":"Sensors"},{"key":"27_CR29","doi-asserted-by":"crossref","unstructured":"Paisitkriangkrai, S., Shen, C., Van Den Hengel, A.: Learning to rank in person re-identification with metric ensembles. In: CVPR, pp. 1846\u20131855 (2015)","DOI":"10.1109\/CVPR.2015.7298794"},{"key":"27_CR30","doi-asserted-by":"crossref","unstructured":"Pu, N., Chen, W., Liu, Y., Bakker, E.M., Lew, M.S.: Dual Gaussian-based variational subspace disentanglement for visible-infrared person re-identification. In: ACMMM, pp. 2149\u20132158 (2020)","DOI":"10.1145\/3394171.3413673"},{"key":"27_CR31","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.1109\/TIFS.2019.2939750","volume":"15","author":"CX Ren","year":"2019","unstructured":"Ren, C.X., Liang, B.H., Lei, Z.: Domain adaptive person re-identification via camera style generation and label propagation. IEEE Trans. Inf. Forensics Secur. 15, 1290\u20131302 (2019)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"27_CR32","doi-asserted-by":"crossref","unstructured":"Sun, D., Yao, A., Zhou, A., Zhao, H.: Deeply-supervised knowledge synergy. In: CVPR, pp. 6997\u20137006 (2019)","DOI":"10.1109\/CVPR.2019.00716"},{"key":"27_CR33","doi-asserted-by":"crossref","unstructured":"Sun, X., Zheng, L.: Dissecting person re-identification from the viewpoint of viewpoint. In: CVPR, pp. 608\u2013617 (2019)","DOI":"10.1109\/CVPR.2019.00070"},{"key":"27_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/978-3-030-01225-0_30","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Sun","year":"2018","unstructured":"Sun, Y., Zheng, L., Yang, Y., Tian, Q., Wang, S.: Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 501\u2013518. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01225-0_30"},{"key":"27_CR35","doi-asserted-by":"crossref","unstructured":"Wang, G.A., et al.: Cross-modality paired-images generation for RGB-infrared person re-identification. In: AAAI, pp. 12144\u201312151 (2020)","DOI":"10.1609\/aaai.v34i07.6894"},{"key":"27_CR36","doi-asserted-by":"crossref","unstructured":"Wang, G., et al.: High-order information matters: learning relation and topology for occluded person re-identification. In: CVPR, pp. 6449\u20136458 (2020)","DOI":"10.1109\/CVPR42600.2020.00648"},{"key":"27_CR37","doi-asserted-by":"crossref","unstructured":"Wang, G., Zhang, T., Cheng, J., Liu, S., Yang, Y., Hou, Z.: RGB-infrared cross-modality person re-identification via joint pixel and feature alignment. In: ICCV, pp. 3623\u20133632 (2019)","DOI":"10.1109\/ICCV.2019.00372"},{"key":"27_CR38","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhu, X., Gong, S., Li, W.: Transferable joint attribute-identity deep learning for unsupervised person re-identification. In: CVPR, pp. 2275\u20132284 (2018)","DOI":"10.1109\/CVPR.2018.00242"},{"key":"27_CR39","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chen, Z., Feng, W., Gang, W.: Person re-identification with cascaded pairwise convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00159"},{"key":"27_CR40","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wang, Z., Zheng, Y., Chuang, Y.Y., Satoh, S.: Learning to reduce dual-level discrepancy for infrared-visible person re-identification. In: CVPR, pp. 618\u2013626 (2019)","DOI":"10.1109\/CVPR.2019.00071"},{"key":"27_CR41","doi-asserted-by":"crossref","unstructured":"Wei, Z., Yang, X., Wang, N., Gao, X.: Syncretic modality collaborative learning for visible infrared person re-identification. In: ICCV, pp. 225\u2013234, October 2021","DOI":"10.1109\/ICCV48922.2021.00029"},{"issue":"6","key":"27_CR42","doi-asserted-by":"publisher","first-page":"1765","DOI":"10.1007\/s11263-019-01290-1","volume":"128","author":"A Wu","year":"2020","unstructured":"Wu, A., Zheng, W.-S., Gong, S., Lai, J.: RGB-IR person re-identification by cross-modality similarity preservation. IJCV 128(6), 1765\u20131785 (2020). https:\/\/doi.org\/10.1007\/s11263-019-01290-1","journal-title":"IJCV"},{"key":"27_CR43","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: ICCV, pp. 5380\u20135389 (2017)","DOI":"10.1109\/ICCV.2017.575"},{"key":"27_CR44","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1109\/TIFS.2021.3075894","volume":"17","author":"D Wu","year":"2021","unstructured":"Wu, D., Ye, M., Lin, G., Gao, X., Shen, J.: Person re-identification by context-aware part attention and multi-head collaborative learning. IEEE Trans. Inf. Forensics Secur. 17, 115\u2013126 (2021)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"27_CR45","doi-asserted-by":"crossref","unstructured":"Wu, Q., et al.: Discover cross-modality nuances for visible-infrared person re-identification. In: CVPR, pp. 4330\u20134339, June 2021","DOI":"10.1109\/CVPR46437.2021.00431"},{"key":"27_CR46","doi-asserted-by":"crossref","unstructured":"Xuan, S., Zhang, S.: Intra-inter camera similarity for unsupervised person re-identification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11926\u201311935 (2021)","DOI":"10.1109\/CVPR46437.2021.01175"},{"key":"27_CR47","first-page":"9387","volume":"29","author":"M Ye","year":"2020","unstructured":"Ye, M., Lan, X., Leng, Q., Shen, J.: Cross-modality person re-identification via modality-aware collaborative ensemble learning. IEEE TIP 29, 9387\u20139399 (2020)","journal-title":"IEEE TIP"},{"key":"27_CR48","doi-asserted-by":"crossref","unstructured":"Ye, M., Lan, X., Li, J., Yuen, P.C.: Hierarchical discriminative learning for visible thermal person re-identification. In: AAAI, pp. 7501\u20137508 (2018)","DOI":"10.1609\/aaai.v32i1.12293"},{"key":"27_CR49","first-page":"407","volume":"15","author":"M Ye","year":"2019","unstructured":"Ye, M., Lan, X., Wang, Z., Yuen, P.C.: Bi-directional center-constrained top-ranking for visible thermal person re-identification. IEEE TIFS 15, 407\u2013419 (2019)","journal-title":"IEEE TIFS"},{"key":"27_CR50","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/978-3-030-58520-4_14","volume-title":"Computer Vision \u2013 ECCV 2020","author":"M Ye","year":"2020","unstructured":"Ye, M., Shen, J., J. Crandall, D., Shao, L., Luo, J.: Dynamic dual-attentive aggregation learning for visible-infrared person re-identification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12362, pp. 229\u2013247. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58520-4_14"},{"key":"27_CR51","unstructured":"Ye, M., Shen, J., Lin, G., Xiang, T., Shao, L., Hoi, S.C.H.: Deep learning for person re-identification: a survey and outlook. arXiv preprint arXiv:2001.04193 (2020)"},{"key":"27_CR52","first-page":"728","volume":"16","author":"M Ye","year":"2020","unstructured":"Ye, M., Shen, J., Shao, L.: Visible-infrared person re-identification via homogeneous augmented tri-modal learning. IEEE TIFS 16, 728\u2013739 (2020)","journal-title":"IEEE TIFS"},{"key":"27_CR53","doi-asserted-by":"crossref","unstructured":"Yu, S., Li, S., Chen, D., Zhao, R., Yan, J., Qiao, Y.: COCAS: a large-scale clothes changing person dataset for re-identification. In: CVPR, pp. 3400\u20133409 (2020)","DOI":"10.1109\/CVPR42600.2020.00346"},{"key":"27_CR54","doi-asserted-by":"crossref","unstructured":"Zhang, X., Ge, Y., Qiao, Y., Li, H.: Refining pseudo labels with clustering consensus over generations for unsupervised object re-identification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3436\u20133445 (2021)","DOI":"10.1109\/CVPR46437.2021.00344"},{"key":"27_CR55","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Lan, C., Zeng, W., Chen, Z.: Multi-granularity reference-aided attentive feature aggregation for video-based person re-identification. In: CVPR, pp. 10407\u201310416 (2020)","DOI":"10.1109\/CVPR42600.2020.01042"},{"key":"27_CR56","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Lan, C., Zeng, W., Jin, X., Chen, Z.: Relation-aware global attention for person re-identification. In: CVPR, pp. 3186\u20133195 (2020)","DOI":"10.1109\/CVPR42600.2020.00325"},{"key":"27_CR57","doi-asserted-by":"crossref","unstructured":"Zheng, F., et al.: Pyramidal person re-identification via multi-loss dynamic training. In: CVPR, pp. 8514\u20138522 (2019)","DOI":"10.1109\/CVPR.2019.00871"},{"key":"27_CR58","doi-asserted-by":"crossref","unstructured":"Zheng, M., Karanam, S., Wu, Z., Radke, R.J.: Re-identification with consistent attentive Siamese networks. In: CVPR, pp. 5735\u20135744 (2019)","DOI":"10.1109\/CVPR.2019.00588"},{"key":"27_CR59","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., Zheng, Z., Li, S., Yang, Y.: Camera style adaptation for person re-identification. In: CVPR, pp. 5157\u20135166 (2018)","DOI":"10.1109\/CVPR.2018.00541"},{"key":"27_CR60","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1109\/TIFS.2017.2765524","volume":"13","author":"X Zhu","year":"2017","unstructured":"Zhu, X., Jing, X.Y., You, X., Zuo, W., Shan, S., Zheng, W.S.: Image to video person re-identification by learning heterogeneous dictionary pair with feature projection matrix. IEEE Trans. Inf. Forensics Secur. 13, 717\u2013732 (2017)","journal-title":"IEEE Trans. Inf. Forensics Secur."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19781-9_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:40:03Z","timestamp":1710261603000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19781-9_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031197802","9783031197819"],"references-count":60,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19781-9_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"23 October 2022","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)"}}]}}