{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T01:25:54Z","timestamp":1773105954436,"version":"3.50.1"},"publisher-location":"Cham","reference-count":46,"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_34","type":"book-chapter","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T10:04:02Z","timestamp":1602669842000},"page":"407-419","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Residual Attention SiameseRPN for Visual Tracking"],"prefix":"10.1007","author":[{"given":"Xu","family":"Cheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enlu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhangjie","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,15]]},"reference":[{"key":"34_CR1","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.patcog.2017.11.007","volume":"76","author":"P Li","year":"2018","unstructured":"Li, P., Wang, D., Wang, L., et al.: Deep visual tracking: review and experimental comparison. Pattern Recogn. 76, 323\u2013338 (2018)","journal-title":"Pattern Recogn."},{"key":"34_CR2","unstructured":"Zhu, P., Wen, L., Du, D., et al.: Vision meets drones: past, present and future. arXiv preprint arXiv:2001.06303 (2020)"},{"key":"34_CR3","doi-asserted-by":"crossref","unstructured":"Ma, C., Huang, J., Yang, X., et al.: Hierarchical convolutional features for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, Piscataway, NJ, pp. 3074\u20133082. IEEE (2015)","DOI":"10.1109\/ICCV.2015.352"},{"key":"34_CR4","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., Khan, F., et al.: Convolutional features for correlation filter based visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, Piscataway, NJ, pp. 58\u201366. IEEE (2015)","DOI":"10.1109\/ICCVW.2015.84"},{"key":"34_CR5","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Bhat, G., Khan, F., et al.: ECO: efficient convolution operators for tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, NJ, pp. 6638\u20136646. IEEE (2017)","DOI":"10.1109\/CVPR.2017.733"},{"key":"34_CR6","unstructured":"Hong, S., You, T., Kwak, S., et al.: Online tracking by learning discriminative saliency map with convolutional neural network. In: International Conference on Machine Learning, New York, NY, pp. 597\u2013606. ACM (2015)"},{"issue":"4","key":"34_CR7","doi-asserted-by":"publisher","first-page":"1834","DOI":"10.1109\/TIP.2015.2510583","volume":"25","author":"H Li","year":"2015","unstructured":"Li, H., Li, Y., Porikli, F.: DeepTrack: learning discriminative feature representations online for robust visual tracking. IEEE Trans. Image Process. 25(4), 1834\u20131848 (2015)","journal-title":"IEEE Trans. Image Process."},{"key":"34_CR8","doi-asserted-by":"crossref","unstructured":"Wang, L., Ouyang, W., Wang, X., et al.: Visual tracking with fully convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, Piscataway, NJ, pp. 3119\u20133127. IEEE (2015)","DOI":"10.1109\/ICCV.2015.357"},{"key":"34_CR9","doi-asserted-by":"crossref","unstructured":"Teng, Z., Xing, J., Wang, Q., et al.: Robust object tracking based on temporal and spatial deep networks. In: Proceedings of the IEEE International Conference on Computer Vision, Piscataway, NJ, pp. 1144\u20131153. IEEE (2017)","DOI":"10.1109\/ICCV.2017.130"},{"key":"34_CR10","doi-asserted-by":"crossref","unstructured":"Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, NJ, pp. 4293\u20134302. IEEE (2016)","DOI":"10.1109\/CVPR.2016.465"},{"key":"34_CR11","doi-asserted-by":"crossref","unstructured":"Valmadre, J., Bertinetto, L., Henriques, J., et al.: End-to-end representation learning for correlation filter based tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, NJ, pp. 2805\u20132813. IEEE (2017)","DOI":"10.1109\/CVPR.2017.531"},{"key":"34_CR12","doi-asserted-by":"crossref","unstructured":"Cui, Z., Xiao, S., Feng, J., et al.: Recurrently target-attending tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, NJ, pp. 1449\u20131458. IEEE (2016)","DOI":"10.1109\/CVPR.2016.161"},{"key":"34_CR13","doi-asserted-by":"crossref","unstructured":"Choi, J., Chang, H., Yun, S., et al.: Attentional correlation filter network for adaptive visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, NJ, pp. 4807\u20134816. IEEE (2017)","DOI":"10.1109\/CVPR.2017.513"},{"key":"34_CR14","doi-asserted-by":"crossref","unstructured":"Tao, R., Gavves, E., Smeulders, A.: Siamese instance search for tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, NJ, pp. 1420\u20131429. IEEE (2016)","DOI":"10.1109\/CVPR.2016.158"},{"key":"34_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1007\/978-3-319-48881-3_56","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"L Bertinetto","year":"2016","unstructured":"Bertinetto, L., Valmadre, J., Henriques, Jo\u00e3o F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional siamese networks for object tracking. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 850\u2013865. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-48881-3_56"},{"key":"34_CR16","doi-asserted-by":"crossref","unstructured":"Li, B., Wu, W., Wang, Q., et al.: SiamRPN++: evolution of Siamese visual tracking with very deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, NJ, pp. 4282\u20134291. IEEE (2019)","DOI":"10.1109\/CVPR.2019.00441"},{"key":"34_CR17","doi-asserted-by":"crossref","unstructured":"Li, B., Yan, J., Wu, W., et al.: High performance visual tracking with Siamese region proposal network. In; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, NJ, pp. 8971\u20138980. IEEE (2018)","DOI":"10.1109\/CVPR.2018.00935"},{"key":"34_CR18","unstructured":"Zhou, W., Wen, L., Zhang, L., et al.: SiamMan: Siamese motion-aware network for visual tracking. arXiv preprint arXiv:1912.05515 (2019)"},{"key":"34_CR19","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Peng, H.: Deeper and wider Siamese networks for real-time visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, NJ, pp. 4591\u20134600. IEEE (2019)","DOI":"10.1109\/CVPR.2019.00472"},{"key":"34_CR20","doi-asserted-by":"crossref","unstructured":"Wang, X., Li, C., Luo, B., et al.: SINT++: robust visual tracking via adversarial positive instance generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, NJ, pp. 4864\u20134873. IEEE (2018)","DOI":"10.1109\/CVPR.2018.00511"},{"key":"34_CR21","doi-asserted-by":"crossref","unstructured":"Song, Y., Ma, C., Wu, X., et al.: VITAL: visual tracking via adversarial learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, NJ, pp. 8990\u20138999. IEEE (2018)","DOI":"10.1109\/CVPR.2018.00937"},{"issue":"3","key":"34_CR22","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","volume":"37","author":"J Henriques","year":"2014","unstructured":"Henriques, J., Caseiro, R., Martins, P., et al.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583\u2013596 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"8","key":"34_CR23","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1109\/TPAMI.2016.2609928","volume":"39","author":"M Danelljan","year":"2016","unstructured":"Danelljan, M., H\u00e4ger, G., Khan, F., et al.: Discriminative scale space tracking. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1561\u20131575 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"34_CR24","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., Khan, F., et al.: Learning spatially regularized correlation filters for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, Piscataway, NJ, pp. 4310\u20134318 IEEE (2015)","DOI":"10.1109\/ICCV.2015.490"},{"key":"34_CR25","doi-asserted-by":"crossref","unstructured":"Kiani Galoogahi, H., Sim, T., Lucey, S.: Correlation filters with limited boundaries. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, NJ, pp. 4630\u20134638. IEEE (2015)","DOI":"10.1109\/CVPR.2015.7299094"},{"key":"34_CR26","doi-asserted-by":"crossref","unstructured":"Ma, C., Yang, X., Zhang, C., et al.: Long-term correlation tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, NJ, pp. 5388\u20135396. IEEE (2015)","DOI":"10.1109\/CVPR.2015.7299177"},{"key":"34_CR27","doi-asserted-by":"crossref","unstructured":"Li, F., Tian, C., Zuo, W., et al.: Learning spatial-temporal regularized correlation filters for visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, NJ, pp: 4904\u20134913. IEEE (2018)","DOI":"10.1109\/CVPR.2018.00515"},{"key":"34_CR28","doi-asserted-by":"crossref","unstructured":"Gladh, S., Danelljan, M., Khan, F., et al.: Deep motion features for visual tracking. In: 23rd International Conference on Pattern Recognition, Piscataway, NJ, pp. 1243\u20131248. IEEE (2016)","DOI":"10.1109\/ICPR.2016.7899807"},{"key":"34_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1007\/978-3-319-46454-1_29","volume-title":"Computer Vision \u2013 ECCV 2016","author":"M Danelljan","year":"2016","unstructured":"Danelljan, M., Robinson, A., Shahbaz Khan, F., Felsberg, M.: Beyond correlation filters: learning continuous convolution operators for visual tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 472\u2013488. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46454-1_29"},{"key":"34_CR30","doi-asserted-by":"crossref","unstructured":"Lukezic, A., Vojir, T., Zajc, L., et al.: Discriminative correlation filter with channel and spatial reliability. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, NJ, pp. 6309\u20136318. IEEE (2017)","DOI":"10.1109\/CVPR.2017.515"},{"key":"34_CR31","unstructured":"Wang, Q., Gao, J., Xing, J., et al.: DCFNet: discriminant correlation filters network for visual tracking. arXiv preprint arXiv:1704.04057 (2017)"},{"key":"34_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1007\/978-3-319-16181-5_18","volume-title":"Computer Vision - ECCV 2014 Workshops","author":"Y Li","year":"2015","unstructured":"Li, Y., Zhu, J.: A scale adaptive kernel correlation filter tracker with feature integration. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8926, pp. 254\u2013265. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-16181-5_18"},{"key":"34_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, T., Xu, C., Yang, M.: Multi-task correlation particle filter for robust object tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, NJ, pp. 4335\u20134343. IEEE (2017)","DOI":"10.1109\/CVPR.2017.512"},{"key":"34_CR34","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Bhat, G., Khan, F., et al. Atom: accurate tracking by overlap maximization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, NJ, pp. 4660\u20134669. IEEE (2019)","DOI":"10.1109\/CVPR.2019.00479"},{"key":"34_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-11009-3_1","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"M Kristan","year":"2019","unstructured":"Kristan, M., et al.: The sixth visual object tracking VOT2018 challenge results. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11129, pp. 3\u201353. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11009-3_1"},{"key":"34_CR36","doi-asserted-by":"crossref","unstructured":"Dai, K., Zhang, Y., Wang, D., et al.: High-performance long-term tracking with meta-updater. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Piscataway, NJ (2020)","DOI":"10.1109\/CVPR42600.2020.00633"},{"key":"34_CR37","doi-asserted-by":"crossref","unstructured":"Fan, H., Lin, L., Yang, F., et al.: LaSOT: a high-quality benchmark for large-scale single object tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, NJ, pp. 5374\u20135383. IEEE (2019)","DOI":"10.1109\/CVPR.2019.00552"},{"key":"34_CR38","doi-asserted-by":"crossref","unstructured":"Song, Y., Ma, C., Gong, L., et al.: CREST: convolutional residual learning for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, Piscataway, NJ, pp. 2555\u20132564. IEEE (2017)","DOI":"10.1109\/ICCV.2017.279"},{"key":"34_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/978-3-030-01240-3_7","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Z Zhu","year":"2018","unstructured":"Zhu, Z., Wang, Q., Li, B., Wu, W., Yan, J., Hu, W.: Distractor-aware Siamese networks for visual object tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 103\u2013119. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01240-3_7"},{"issue":"9","key":"34_CR40","doi-asserted-by":"publisher","first-page":"1834","DOI":"10.1109\/TPAMI.2014.2388226","volume":"37","author":"Y Wu","year":"2015","unstructured":"Wu, Y., Lim, J., Yang, M.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1834\u20131848 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"34_CR41","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/978-3-319-46448-0_27","volume-title":"Computer Vision \u2013 ECCV 2016","author":"M Mueller","year":"2016","unstructured":"Mueller, M., Smith, N., Ghanem, B.: A benchmark and simulator for UAV tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 445\u2013461. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_27"},{"key":"34_CR42","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1007\/978-3-030-01234-2_20","volume-title":"Computer Vision \u2013 ECCV 2018","author":"B Chen","year":"2018","unstructured":"Chen, B., Wang, D., Li, P., Wang, S., Lu, H.: Real-time \u2018actor-critic\u2019 tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 328\u2013345. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_20"},{"key":"34_CR43","doi-asserted-by":"crossref","unstructured":"Sun, C., Wang, D., Lu, H., et al.: Learning spatial-aware regressions for visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, NJ, pp. 8962\u20138970. IEEE (2018)","DOI":"10.1109\/CVPR.2018.00934"},{"key":"34_CR44","unstructured":"Pu, S., Song, Y., Ma, C., et al.: Deep attentive tracking via reciprocative learning. In: Neural Information Processing Systems, pp. 1931\u20131941. MIT Press, Cambridge (2018)"},{"key":"34_CR45","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1007\/978-3-030-01246-5_19","volume-title":"Computer Vision \u2013 ECCV 2018","author":"M M\u00fcller","year":"2018","unstructured":"M\u00fcller, M., Bibi, A., Giancola, S., Alsubaihi, S., Ghanem, B.: TrackingNet: a large-scale dataset and benchmark for object tracking in the wild. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 310\u2013327. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01246-5_19"},{"key":"34_CR46","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1007\/978-3-030-01216-8_30","volume-title":"Computer Vision \u2013 ECCV 2018","author":"G Bhat","year":"2018","unstructured":"Bhat, G., Johnander, J., Danelljan, M., Khan, F.S., Felsberg, M.: Unveiling the power of deep tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 493\u2013509. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01216-8_30"}],"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_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T22:04:37Z","timestamp":1760393077000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-60639-8_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030606381","9783030606398"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60639-8_34","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)"}}]}}