{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T20:17:57Z","timestamp":1769631477596,"version":"3.49.0"},"publisher-location":"Cham","reference-count":57,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031250682","type":"print"},{"value":"9783031250699","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-25069-9_37","type":"book-chapter","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T00:15:46Z","timestamp":1676333746000},"page":"576-593","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["SegTAD: Precise Temporal Action Detection via\u00a0Semantic Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4993-5416","authenticated-orcid":false,"given":"Chen","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6234-0831","authenticated-orcid":false,"given":"Merey","family":"Ramazanova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9152-4632","authenticated-orcid":false,"given":"Mengmeng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5534-587X","authenticated-orcid":false,"given":"Bernard","family":"Ghanem","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,14]]},"reference":[{"key":"37_CR1","unstructured":"Report of Temporal Action Proposal. http:\/\/hacs.csail.mit.edu\/challenge\/challenge19_report_runnerup.pdf (2020)"},{"key":"37_CR2","doi-asserted-by":"crossref","unstructured":"Alcazar, J.L., Cordes, M., Zhao, C., Ghanem, B.: End-to-end active speaker detection. In: Proceedings of European Conference on Computer Vision (ECCV) (2022)","DOI":"10.1007\/978-3-031-19836-6_8"},{"key":"37_CR3","doi-asserted-by":"crossref","unstructured":"Buch, S., Escorcia, V., Shen, C., Ghanem, B., Niebles, J.C.: SST: single-stream temporal action proposals. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.675"},{"key":"37_CR4","doi-asserted-by":"crossref","unstructured":"Carreira, J., Zisserman, A.: Quo vadis, action recognition? A new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.502"},{"key":"37_CR5","doi-asserted-by":"crossref","unstructured":"Chao, Y.W., Vijayanarasimhan, S., Seybold, B., Ross, D.A., Deng, J., Sukthankar, R.: Rethinking the faster R-CNN architecture for temporal action localization. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00124"},{"issue":"12","key":"37_CR6","doi-asserted-by":"publisher","first-page":"3205","DOI":"10.1109\/TMM.2019.2916104","volume":"21","author":"C Chen","year":"2019","unstructured":"Chen, C., Ling, Q.: Adaptive convolution for object detection. IEEE Trans. Multimed. (TMM) 21(12), 3205\u20133217 (2019). https:\/\/doi.org\/10.1109\/TMM.2019.2916104","journal-title":"IEEE Trans. Multimed. (TMM)"},{"key":"37_CR7","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. ArXiv abs\/1706.05587 (2017)"},{"key":"37_CR8","doi-asserted-by":"crossref","unstructured":"Dai, X., Singh, B., Zhang, G., Davis, L.S., Qiu Chen, Y.: Temporal context network for activity localization in videos. In: Proceedings of IEEE International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.610"},{"key":"37_CR9","doi-asserted-by":"crossref","unstructured":"Escorcia, V., Heilbron, F.C., Niebles, J.C., Ghanem, B.: DAPs: deep action proposals for action understanding. In: Proceedings of European Conference on Computer Vision (ECCV) (2016)","DOI":"10.1007\/978-3-319-46487-9_47"},{"key":"37_CR10","doi-asserted-by":"crossref","unstructured":"Caba Heilbron, F., Victor Escorcia, B.G., Niebles, J.C.: Activitynet: a large-scale video benchmark for human activity understanding. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7298698"},{"key":"37_CR11","doi-asserted-by":"crossref","unstructured":"Gao, J., Chen, K., Nevatia, R.: CTAP: complementary temporal action proposal generation. In: Proceedings of European Conference on Computer Vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01216-8_5"},{"key":"37_CR12","doi-asserted-by":"crossref","unstructured":"Girshick, R.B.: Fast R-CNN. In: Proceedings of IEEE International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"37_CR13","doi-asserted-by":"crossref","unstructured":"Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"37_CR14","unstructured":"Grauman, K., et al.: Ego4D: around the world in 3,000 hours of egocentric video. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18995\u201319012 (2022)"},{"key":"37_CR15","unstructured":"Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of Neural Information Processing Systems (NeurIPS) (2017)"},{"key":"37_CR16","doi-asserted-by":"crossref","unstructured":"Heilbron, F.C., Barrios, W., Escorcia, V., Ghanem, B.: SCC: semantic context cascade for efficient action detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.338"},{"key":"37_CR17","doi-asserted-by":"crossref","unstructured":"Heilbron, F.C., Niebles, J.C., Ghanem, B.: Fast temporal activity proposals for efficient detection of human actions in untrimmed videos. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.211"},{"issue":"9","key":"37_CR18","doi-asserted-by":"publisher","first-page":"2478","DOI":"10.1109\/TMM.2018.2798282","volume":"20","author":"B Kang","year":"2018","unstructured":"Kang, B., Lee, Y., Nguyen, T.Q.: Depth-adaptive deep neural network for semantic segmentation. IEEE Trans. Multimed. (TMM) 20(9), 2478\u20132490 (2018). https:\/\/doi.org\/10.1109\/TMM.2018.2798282","journal-title":"IEEE Trans. Multimed. (TMM)"},{"issue":"11","key":"37_CR19","doi-asserted-by":"publisher","first-page":"2990","DOI":"10.1109\/TMM.2020.2965434","volume":"22","author":"J Li","year":"2020","unstructured":"Li, J., Liu, X., Zhang, W., Zhang, M., Song, J., Sebe, N.: Spatio-temporal attention networks for action recognition and detection. IEEE Trans. Multimed. (TMM) 22(11), 2990\u20133001 (2020). https:\/\/doi.org\/10.1109\/TMM.2020.2965434","journal-title":"IEEE Trans. Multimed. (TMM)"},{"issue":"4","key":"37_CR20","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1109\/TMM.2018.2867720","volume":"21","author":"Y Li","year":"2019","unstructured":"Li, Y., Guo, Y., Guo, J., Ma, Z., Kong, X., Liu, Q.: Joint CRF and locality-consistent dictionary learning for semantic segmentation. IEEE Trans. Multimed. (TMM) 21(4), 875\u2013886 (2019). https:\/\/doi.org\/10.1109\/TMM.2018.2867720","journal-title":"IEEE Trans. Multimed. (TMM)"},{"key":"37_CR21","doi-asserted-by":"crossref","unstructured":"Lin, J., Gan, C., Han, S.: TSM: temporal shift module for efficient video understanding. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00718"},{"key":"37_CR22","doi-asserted-by":"crossref","unstructured":"Lin, T., Liu, X., Li, X., Ding, E., Wen, S.: BMN: boundary-matching network for temporal action proposal generation. In: Proceedings of IEEE International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00399"},{"key":"37_CR23","doi-asserted-by":"crossref","unstructured":"Lin, T., Zhao, X., Shou, Z.: Single shot temporal action detection. In: Proceedings of ACM International Conference on Multimedia (ACM MM) (2017)","DOI":"10.1145\/3123266.3123343"},{"key":"37_CR24","unstructured":"Lin, T., Zhao, X., Shou, Z.: Temporal convolution based action proposal: submission to ActivityNet 2017. ActivityNet Large Scale Activity Recognition Challenge workshop at CVPR (2017)"},{"key":"37_CR25","doi-asserted-by":"crossref","unstructured":"Lin, T., Zhao, X., Su, H., Wang, C., Yang, M.: BSN: boundary sensitive network for temporal action proposal generation. In: Proceedings of European Conference on Computer Vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01225-0_1"},{"issue":"2","key":"37_CR26","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1109\/TMM.2019.2929923","volume":"22","author":"H Liu","year":"2020","unstructured":"Liu, H., Wang, S., Wang, W., Cheng, J.: Multi-scale based context-aware net for action detection. IEEE Trans. Multimed. (TMM) 22(2), 337\u2013348 (2020). https:\/\/doi.org\/10.1109\/TMM.2019.2929923","journal-title":"IEEE Trans. Multimed. (TMM)"},{"issue":"2","key":"37_CR27","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1109\/TMM.2019.2929923","volume":"22","author":"H Liu","year":"2020","unstructured":"Liu, H., Wang, S., Wang, W., Cheng, J.: Multi-scale based context-aware net for action detection. IEEE Trans. Multimed. (TMM) 22(2), 337\u2013348 (2020)","journal-title":"IEEE Trans. Multimed. (TMM)"},{"key":"37_CR28","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1109\/TMM.2020.2974323","volume":"23","author":"K Liu","year":"2021","unstructured":"Liu, K., Gao, L., Khan, N.M., Qi, L., Guan, L.: A multi-stream graph convolutional networks-hidden conditional random field model for skeleton-based action recognition. IEEE Trans. Multimed. (TMM) 23, 64\u201376 (2021). https:\/\/doi.org\/10.1109\/TMM.2020.2974323","journal-title":"IEEE Trans. Multimed. (TMM)"},{"key":"37_CR29","doi-asserted-by":"crossref","unstructured":"Liu, Y., Ma, L., Zhang, Y., Liu, W., Chang, S.F.: Multi-granularity generator for temporal action proposal. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00372"},{"key":"37_CR30","doi-asserted-by":"crossref","unstructured":"Long, F., Yao, T., Qiu, Z., Tian, X., Luo, J., Mei, T.: Gaussian temporal awareness networks for action localization. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00043"},{"key":"37_CR31","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"37_CR32","doi-asserted-by":"crossref","unstructured":"Pardo, A., Caba, F., Alc\u00e1zar, J.L., Thabet, A.K., Ghanem, B.: Learning to cut by watching movies. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6858\u20136868 (2021)","DOI":"10.1109\/ICCV48922.2021.00678"},{"key":"37_CR33","doi-asserted-by":"crossref","unstructured":"Pardo, A., Heilbron, F.C., Alc\u00e1zar, J.L., Thabet, A., Ghanem, B.: Moviecuts: a new dataset and benchmark for cut type recognition. arXiv preprint arXiv:2109.05569 (2021)","DOI":"10.1007\/978-3-031-20071-7_39"},{"issue":"12","key":"37_CR34","doi-asserted-by":"publisher","first-page":"3039","DOI":"10.1109\/TMM.2020.2971175","volume":"22","author":"H Qiu","year":"2020","unstructured":"Qiu, H., et al.: Hierarchical context features embedding for object detection. IEEE Trans. Multimed. (TMM) 22(12), 3039\u20133050 (2020). https:\/\/doi.org\/10.1109\/TMM.2020.2971175","journal-title":"IEEE Trans. Multimed. (TMM)"},{"key":"37_CR35","unstructured":"Ramazanova, M., Escorcia, V., Heilbron, F.C., Zhao, C., Ghanem, B.: Owl (observe, watch, listen): localizing actions in egocentric video via audiovisual temporal context. ArXiv abs\/2202.04947 (2022)"},{"issue":"6","key":"37_CR36","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"37_CR37","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Proceedings of Medical Image Computing and Computer-Assisted Intervention (MICCAI) (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"37_CR38","doi-asserted-by":"crossref","unstructured":"Shou, Z., Chan, J., Zareian, A., Miyazawa, K., Chang, S.F.: CDC: convolutional-de-convolutional networks for precise temporal action localization in untrimmed videos. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.155"},{"key":"37_CR39","doi-asserted-by":"crossref","unstructured":"Shou, Z., Wang, D., Chang, S.F.: Temporal action localization in untrimmed videos via multi-stage CNNs. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.119"},{"key":"37_CR40","unstructured":"Singh, G., Cuzzolin, F.: Untrimmed video classification for activity detection: submission to ActivityNet Challenge. ActivityNet Large Scale Activity Recognition Challenge workshop at CVPR (2016)"},{"key":"37_CR41","doi-asserted-by":"crossref","unstructured":"Soldan, M., Pardo, A., Alc\u00e1zar, J.L., Caba, F., Zhao, C., Giancola, S., Ghanem, B.: Mad: a scalable dataset for language grounding in videos from movie audio descriptions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5026\u20135035 (2022)","DOI":"10.1109\/CVPR52688.2022.00497"},{"key":"37_CR42","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.1109\/TMM.2020.2999184","volume":"23","author":"H Su","year":"2021","unstructured":"Su, H., Zhao, X., Lin, T., Liu, S., Hu, Z.: Transferable knowledge-based multi-granularity fusion network for weakly supervised temporal action detection. IEEE Trans. Multimed. (TMM) 23, 1503\u20131515 (2021). https:\/\/doi.org\/10.1109\/TMM.2020.2999184","journal-title":"IEEE Trans. Multimed. (TMM)"},{"key":"37_CR43","doi-asserted-by":"crossref","unstructured":"Wang, L., Xiong, Y., Lin, D., Van Gool, L.: Untrimmednets for weakly supervised action recognition and detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.678"},{"key":"37_CR44","unstructured":"Wang, R., Tao, D.: UTS at ActivityNet 2016. ActivityNet Large Scale Activity Recognition Challenge (2016)"},{"issue":"5","key":"37_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3326362","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. ACM Trans. Graph. (TOG) 38(5), 1\u201312 (2019)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"37_CR46","unstructured":"Xiong, Y., et al.: CUHK & ETHZ & SIAT submission to ActivityNet Challenge 2016. arXiv:1608.00797 (2016)"},{"key":"37_CR47","doi-asserted-by":"crossref","unstructured":"Xu, H., Das, A., Saenko, K.: R-C3D: region convolutional 3D network for temporal activity detection. In: Proceedings of IEEE International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.617"},{"key":"37_CR48","doi-asserted-by":"crossref","unstructured":"Xu, M., Zhao, C., Rojas, D.S., Thabet, A., Ghanem, B.: G-TAD: sub-graph localization for temporal action detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.01017"},{"key":"37_CR49","unstructured":"Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: Proceedings of International Conference on Learning Representations (ICLR) (2016)"},{"key":"37_CR50","doi-asserted-by":"crossref","unstructured":"Yuan, Z.H., Stroud, J.C., Lu, T., Deng, J.: Temporal action localization by structured maximal sums. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.342"},{"key":"37_CR51","doi-asserted-by":"crossref","unstructured":"Zeng, R., Huang, W., Tan, M., Rong, Y., Zhao, P., Huang, J., Gan, C.: Graph convolutional networks for temporal action localization. In: Proceedings of IEEE International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00719"},{"key":"37_CR52","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., Li, S.Z.: Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"37_CR53","unstructured":"Zhang, S., Peng, H., Yang, L., Fu, J., Luo, J.: Learning sparse 2d temporal adjacent networks for temporal action localization. In: HACS Temporal Action Localization Challenge at IEEE International Conference on Computer Vision (ICCV) (2019)"},{"key":"37_CR54","doi-asserted-by":"crossref","unstructured":"Zhao, C., Thabet, A.K., Ghanem, B.: Video self-stitching graph network for temporal action localization. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 13658\u201313667 (2021)","DOI":"10.1109\/ICCV48922.2021.01340"},{"key":"37_CR55","doi-asserted-by":"crossref","unstructured":"Zhao, H., Yan, Z., Torresani, L., Torralba, A.: HACS: human action clips and segments dataset for recognition and temporal localization. In: Proceedings of IEEE International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00876"},{"key":"37_CR56","doi-asserted-by":"crossref","unstructured":"Zhao, P., Xie, L., Ju, C., Zhang, Y., Wang, Y., Tian, Q.: Bottom-up temporal action localization with mutual regularization. In: Proceedings of European Conference on Computer Vision (ECCV) (2020)","DOI":"10.1007\/978-3-030-58598-3_32"},{"key":"37_CR57","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Xiong, Y., Wang, L., Wu, Z., Tang, X., Lin, D.: Temporal action detection with structured segment networks. In: Proceedings of IEEE International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.317"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25069-9_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T12:56:32Z","timestamp":1709816192000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25069-9_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250682","9783031250699"],"references-count":57,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25069-9_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"14 February 2023","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)"}},{"value":"From the workshops, 367 reviewed full papers have been selected for publication","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)"}}]}}