{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:53:51Z","timestamp":1773708831622,"version":"3.50.1"},"publisher-location":"Cham","reference-count":67,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030606381","type":"print"},{"value":"9783030606398","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-60639-8_16","type":"book-chapter","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T10:04:02Z","timestamp":1602669842000},"page":"187-199","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-model Network for Fine-Grained Cross-Media Retrieval"],"prefix":"10.1007","author":[{"given":"Jiemi","family":"Bai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yazhou","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yichao","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wankou","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fumin","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,15]]},"reference":[{"issue":"6","key":"16_CR1","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1109\/TKDE.2019.2903036","volume":"32","author":"Y Yao","year":"2020","unstructured":"Yao, Y., et al.: Towards automatic construction of diverse, high-quality image dataset. IEEE Trans. Knowl. Data Eng. 32(6), 1199\u20131211 (2020)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Lu, J., et al.: HSI Road: a hyper spectral image dataset for road segmentation. In: IEEE International Conference on Multimedia and Expo, pp. 1\u20136 (2020)","DOI":"10.1109\/ICME46284.2020.9102890"},{"key":"16_CR3","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.neucom.2016.07.066","volume":"236","author":"X Hua","year":"2017","unstructured":"Hua, X., et al.: A new web-supervised method for image dataset constructions. Neurocomputing 236, 23\u201331 (2017)","journal-title":"Neurocomputing"},{"issue":"8","key":"16_CR4","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1109\/TMM.2017.2684626","volume":"19","author":"Y Yao","year":"2017","unstructured":"Yao, Y., et al.: Exploiting web images for dataset construction: a domain robust approach. IEEE Trans. Multimedia 19(8), 1771\u20131784 (2017)","journal-title":"IEEE Trans. Multimedia"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Extracting visual knowledge from the internet: making sense of image data. In: International Conference on Multimedia Modeling, pp. 862\u2013873 (2016)","DOI":"10.1007\/978-3-319-27671-7_72"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Shen, F., et al.: Automatic image dataset construction with multiple textual metadata. In: IEEE International Conference on Multimedia and Expo, pp. 1\u20136 (2016)","DOI":"10.1109\/ICME.2016.7552988"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Yao, Y., et al.: A domain robust approach for image dataset construction. In: ACM International conference on Multimedia, pp. 212\u2013216 (2016)","DOI":"10.1145\/2964284.2967213"},{"issue":"7","key":"16_CR8","first-page":"2348","volume":"31","author":"Y Yao","year":"2020","unstructured":"Yao, Y., et al.: Exploiting web images for multi-output classification: from category to subcategories. IEEE Trans. Neural Netw. Learn. Syst. 31(7), 2348\u20132360 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"4","key":"16_CR9","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1109\/TPAMI.2017.2705122","volume":"40","author":"X Shu","year":"2018","unstructured":"Shu, X., et al.: Personalized age progression with bi-level aging dictionary learning. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 905\u2013917 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Yao, Y., et al.: Bridging the web data and fine-grained visual recognition via alleviating label noise and domain mismatch. In: ACM International Conference on Multimedia (2020)","DOI":"10.1145\/3394171.3413851"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Sun, Z., et al.: CRSSC: salvage reusable samples from noisy data for robust learning. In: ACM International Conference on Multimedia (2020)","DOI":"10.1145\/3394171.3413978"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Zhang, C., et al.: Data-driven meta-set based fine-grained visual recognition. In: ACM International Conference on Multimedia (2020)","DOI":"10.1145\/3394171.3414044"},{"key":"16_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11042-019-07870-0","volume":"1","author":"H Liu","year":"2019","unstructured":"Liu, H., Yao, Y., Sun, Z., Li, X., Jia, K., Tang, Z.: Road segmentation with image-LiDAR data fusion in deep neural network. Multimed. Tools Appl. 1, 1\u201316 (2019). https:\/\/doi.org\/10.1007\/s11042-019-07870-0","journal-title":"Multimed. Tools Appl."},{"issue":"17","key":"16_CR14","doi-asserted-by":"publisher","first-page":"24269","DOI":"10.1007\/s11042-018-6986-1","volume":"78","author":"H Liu","year":"2018","unstructured":"Liu, H., Han, X., Li, X., Yao, Y., Huang, P., Tang, Z.: Deep representation learning for road detection using Siamese network. Multimed. Tools. Appl. 78(17), 24269\u201324283 (2018). https:\/\/doi.org\/10.1007\/s11042-018-6986-1","journal-title":"Multimed. Tools. Appl."},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Xu, M., et al.: Deep learning for person reidentification using support vector machines. Adv. Multimed. (2017)","DOI":"10.1155\/2017\/9874345"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Chen, T., et al.: Classification constrained discriminator for domain adaptive semantic segmentation. In: IEEE International Conference on Multimedia and Expo, pp. 1\u20136 (2020)","DOI":"10.1109\/ICME46284.2020.9102965"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Ding, L., et al.: Approximate kernel selection via matrix approximation. In: IEEE Transactions on Neural Networks and Learning Systems (2020)","DOI":"10.1109\/TNNLS.2019.2958922"},{"key":"16_CR18","unstructured":"Shu, X., et al.: Hierarchical long short-term concurrent memory for human interaction recognition. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2019)"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"G.-S. Xie, et al., SRSC: Selective, robust, and supervised constrained feature representation for image classification. IEEE Trans. Neural Netwk. Learn. Syst. (2019)","DOI":"10.1109\/TNNLS.2019.2953675"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Sun, Z., et al.: Dynamically visual disambiguation of keyword-based image search. In: International Joint Conference on Artificial Intelligence, pp. 996\u20131002 (2019)","DOI":"10.24963\/ijcai.2019\/140"},{"key":"16_CR21","unstructured":"Yang, W., et al.: Discovering and distinguishing multiple visual senses for polysemous words. In: AAAI Conference on Artificial Intelligence, pp. 523\u2013530 (2018)"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Hu, B., et al.: PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks. arXiv preprint arXiv:2005.02154 (2020)","DOI":"10.1145\/3394171.3414537"},{"key":"16_CR23","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.neucom.2019.08.050","volume":"368","author":"Y Gu","year":"2019","unstructured":"Gu, Y., et al.: Clustering-driven unsupervised deep hashing for image retrieval. Neurocomputing 368, 114\u2013123 (2019)","journal-title":"Neurocomputing"},{"key":"16_CR24","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: Set and rebase: determining the semantic graph connectivity for unsupervised cross modal hashing. In: International Joint Conference on Artificial Intelligence, pp. 853\u2013859 (2020)","DOI":"10.24963\/ijcai.2020\/119"},{"key":"16_CR25","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.dsp.2018.02.009","volume":"76","author":"P Huang","year":"2018","unstructured":"Huang, P., et al.: Collaborative representation based local discriminant projection for feature extraction. Digit. Signal Process. 76, 84\u201393 (2018)","journal-title":"Digit. Signal Process."},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Extracting privileged information from untagged corpora for classifier learning. In: International Joint Conference on Artificial Intelligence, pp. 1085\u20131091 (2018)","DOI":"10.24963\/ijcai.2018\/151"},{"issue":"1","key":"16_CR27","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1109\/TMM.2018.2847248","volume":"21","author":"Y Yao","year":"2019","unstructured":"Yao, Y., et al.: Extracting multiple visual senses for web learning. IEEE Trans. Multimed. 21(1), 184\u2013196 (2019)","journal-title":"IEEE Trans. Multimed."},{"issue":"1","key":"16_CR28","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1109\/TIP.2018.2869721","volume":"28","author":"Y Yao","year":"2019","unstructured":"Yao, Y., et al.: Extracting privileged information for enhancing classifier learning. IEEE Trans. Image Process. 28(1), 436\u2013450 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"16_CR29","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.patrec.2018.05.028","volume":"117","author":"W Yang","year":"2019","unstructured":"Yang, W., et al.: Exploiting textual and visual features for image categorization. Pattern Recogn. Lett. 117, 140\u2013145 (2019)","journal-title":"Pattern Recogn. Lett."},{"key":"16_CR30","unstructured":"Branson, S., et al.: Bird species categorization using pose normalized deep convolutional nets. arXiv preprint arXiv, 1406.2952 (2014)"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"Castrejon, L., et al.: Learning aligned cross-modal representations from weakly aligned data. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2940\u20132949 (2016)","DOI":"10.1109\/CVPR.2016.321"},{"key":"16_CR32","doi-asserted-by":"crossref","unstructured":"Chen, W., et al.: Beyond triplet loss: a deep quadruplet network for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 403\u2013412 (2017)","DOI":"10.1109\/CVPR.2017.145"},{"key":"16_CR33","doi-asserted-by":"crossref","unstructured":"Cui, Y., et al.: Large scale fine-grained categorization and domain-specific transfer learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4109\u20134118 (2018)","DOI":"10.1109\/CVPR.2018.00432"},{"key":"16_CR34","doi-asserted-by":"crossref","unstructured":"Fu, J., et al.: Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4438\u20134446 (2017)","DOI":"10.1109\/CVPR.2017.476"},{"issue":"2","key":"16_CR35","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/s11263-014-0741-5","volume":"111","author":"E Gavves","year":"2015","unstructured":"Gavves, E., et al.: Local alignments for fine-grained categorization. Int. J. Comput. Vis. 111(2), 191\u2013212 (2015)","journal-title":"Int. J. Comput. Vis."},{"issue":"1","key":"16_CR36","first-page":"723","volume":"13","author":"A Gretton","year":"2012","unstructured":"Gretton, A., et al.: A kernel two-sample test. J. Mach. Learn. Res. 13(1), 723\u2013773 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"16_CR37","doi-asserted-by":"crossref","unstructured":"Gu, J., et al.: Look, imagine and match: Improving textual-visual cross-modal retrieval with generative models. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 7181\u20137189 (2018)","DOI":"10.1109\/CVPR.2018.00750"},{"key":"16_CR38","doi-asserted-by":"crossref","unstructured":"He, X., et al.: Fine-grained image classification via combining vision and language. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5994\u20136002 (2017)","DOI":"10.1109\/CVPR.2017.775"},{"key":"16_CR39","doi-asserted-by":"crossref","unstructured":"He, X., et al.: A new benchmark and approach for fine-grained cross-media retrieval. In: ACM International Conference on Multimedia, pp. 1740\u20131748 (2019)","DOI":"10.1145\/3343031.3350974"},{"key":"16_CR40","unstructured":"Huang, X., et al.: Mhtn: Modal-adversarial hybrid transfer network for cross-modal retrieval. IEEE Trans. Cybernet. (2018)"},{"key":"16_CR41","unstructured":"Kim, J., et al.: Learning semantics with deep belief network for cross-language information retrieval. In: Proceedings of COLING 2012: Posters, pp. 579\u2013588 (2012)"},{"key":"16_CR42","doi-asserted-by":"crossref","unstructured":"Lee, K.H., et al.: Stacked cross attention for image-text matching. In: European Conference on Computer Vision, pp. 201\u2013216 (2018)","DOI":"10.1007\/978-3-030-01225-0_13"},{"key":"16_CR43","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., et al.: Bilinear cnn models for fine-grained visual recognition. In: IEEE International Conference on Computer Vision, pp. 1449\u20131457 (2015)","DOI":"10.1109\/ICCV.2015.170"},{"key":"16_CR44","doi-asserted-by":"crossref","unstructured":"Mandal, D., et al.: Generalized semantic preserving hashing for n-label cross-modal retrieval. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4076\u20134084 (2017)","DOI":"10.1109\/CVPR.2017.282"},{"issue":"3","key":"16_CR45","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.1109\/TIP.2017.2774041","volume":"27","author":"Y Peng","year":"2017","unstructured":"Peng, Y., et al.: Object-part attention model for fine-grained image classification. IEEE Trans. Image Process. 27(3), 1487\u20131500 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"16_CR46","unstructured":"Peng, Y., et al.: Cross-media shared representation by hierarchical learning with multiple deep networks. In: IJCAI, pp. 3846\u20133853 (2016)"},{"key":"16_CR47","doi-asserted-by":"crossref","unstructured":"Rasiwasia, N., et al.: A new approach to cross-modal multimedia retrieval. In: ACM International Conference on Multimedia, pp. 251\u2013260 (2010)","DOI":"10.1145\/1873951.1873987"},{"key":"16_CR48","unstructured":"Simonyan, K., et al.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv, 1409.1556 (2015)"},{"key":"16_CR49","doi-asserted-by":"crossref","unstructured":"Wang, B., et al.: Adversarial cross-modal retrieval. In: ACM International Conference on Multimedia, pp. 154\u2013162 (2017)","DOI":"10.1145\/3123266.3123326"},{"key":"16_CR50","doi-asserted-by":"crossref","unstructured":"Zhang, C., et al.: Web-supervised network with softly update-drop training for fine-grained visual classification. In: AAAI Conference on Artificial Intelligence, pp. 12781\u201312788 (2020)","DOI":"10.1609\/aaai.v34i07.6973"},{"key":"16_CR51","doi-asserted-by":"crossref","unstructured":"Xie, G., et al.: Attentive region embedding network for zero-shot learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 9384\u20139393 (2019)","DOI":"10.1109\/CVPR.2019.00961"},{"key":"16_CR52","doi-asserted-by":"crossref","unstructured":"Wang, C., et al.: Deep semantic mapping for cross-modal retrieval. In: IEEE International Conference on Tools with Artificial Intelligence, pp. 234\u2013241 (2015)","DOI":"10.1109\/ICTAI.2015.45"},{"key":"16_CR53","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Learning a discriminative filter bank within a cnn for fine-grained recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4148\u20134157 (2018)","DOI":"10.1109\/CVPR.2018.00436"},{"issue":"2","key":"16_CR54","first-page":"449","volume":"47","author":"Y Wei","year":"2016","unstructured":"Wei, Y., et al.: Cross-modal retrieval with cnn visual features: A new baseline. IEEE Trans. Cybernet. 47(2), 449\u2013460 (2016)","journal-title":"IEEE Trans. Cybernet."},{"key":"16_CR55","doi-asserted-by":"crossref","unstructured":"Xiao, T., et al.: The application of two-level attention models in deep convolutional neural network for fine-grained image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 842\u2013850 (2015)","DOI":"10.1109\/CVPR.2015.7298685"},{"key":"16_CR56","doi-asserted-by":"crossref","unstructured":"Xie, S., et al.: Hyper-class augmented and regularized deep learning for fine-grained image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2645\u20132654 (2015)","DOI":"10.1109\/CVPR.2015.7298880"},{"key":"16_CR57","unstructured":"Yang, S., et al.: Unsupervised template learning for fine-grained object recognition. In: Advances in Neural Information Processing Systems, pp. 3122\u20133130, (2012)"},{"issue":"6","key":"16_CR58","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1109\/TCSVT.2013.2276704","volume":"24","author":"X Zhai","year":"2014","unstructured":"Zhai, X., et al.: Learning cross-media joint representation with sparse and semisupervised regularization. IEEE Trans. Circuits Syst. Video Technol. 24(6), 965\u2013978 (2014)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"16_CR59","doi-asserted-by":"crossref","unstructured":"Zhang, N., et al.: Part-based r-cnns for fine-grained category detection. In: European Conference on Computer Vision, pp. 834\u2013849 (2014)","DOI":"10.1007\/978-3-319-10590-1_54"},{"issue":"4","key":"16_CR60","doi-asserted-by":"publisher","first-page":"1713","DOI":"10.1109\/TIP.2016.2531289","volume":"25","author":"Y Zhang","year":"2016","unstructured":"Zhang, Y., et al.: Weakly supervised fine-grained categorization with part-based image representation. IEEE Trans. Image Process. 25(4), 1713\u20131725 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"16_CR61","doi-asserted-by":"crossref","unstructured":"Zheng, H., et al.: Learning multi-attention convolutional neural network for fine-grained image recognition. In: IEEE International Conference on Computer Vision, pp. 5209\u20135217 (2017)","DOI":"10.1109\/ICCV.2017.557"},{"key":"16_CR62","doi-asserted-by":"crossref","unstructured":"Zhou, P., et al.: Attention-based bidirectional long short-term memory networks for relation classification. In: Annual Meeting of the Association for Computational Linguistics, pp. 207\u2013212 (2016)","DOI":"10.18653\/v1\/P16-2034"},{"key":"16_CR63","doi-asserted-by":"crossref","unstructured":"Zhang, C., et al.: Web-supervised network for fine-grained visual classification. In: IEEE International Conference on Multimedia and Expo, pp. 1\u20136 (2020)","DOI":"10.1109\/ICME46284.2020.9102790"},{"key":"16_CR64","doi-asserted-by":"crossref","unstructured":"Xie, G., et al.: Region graph embedding network for zero-shot learning. In: European Conference on Computer Vision (2020)","DOI":"10.1007\/978-3-030-58548-8_33"},{"key":"16_CR65","doi-asserted-by":"crossref","unstructured":"Zhou, T., et al.: Motion-attentive transition for zero-shot video object segmentation. In: AAAI Conference on Artificial Intelligence (2020)","DOI":"10.1609\/aaai.v34i07.7008"},{"key":"16_CR66","doi-asserted-by":"crossref","unstructured":"Luo, H., et al.: SegEQA: video segmentation based visual attention for embodied question answering. In: IEEE Conference on Computer Vision, pp. 9667\u20139676 (2019)","DOI":"10.1109\/ICCV.2019.00976"},{"key":"16_CR67","unstructured":"Wang, W., et al.: Target-aware adaptive tracking for unsupervised video object segmentation. The DAVIS Challenge on Video Object Segmentation on CVPR workshop (2020)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-60639-8_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T22:03:37Z","timestamp":1760393017000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-60639-8_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030606381","9783030606398"],"references-count":67,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60639-8_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"15 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"16 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.prcv.cn\/index_en.html","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 system","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"402","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":"158","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":"39% - 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":"4","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)"}}]}}