{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:24:18Z","timestamp":1772907858626,"version":"3.50.1"},"publisher-location":"Cham","reference-count":55,"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_35","type":"book-chapter","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T00:15:46Z","timestamp":1676333746000},"page":"539-556","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["STC: Spatio-Temporal Contrastive Learning for\u00a0Video Instance Segmentation"],"prefix":"10.1007","author":[{"given":"Zhengkai","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhangxuan","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinlong","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yabiao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Tai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengjie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liqing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,14]]},"reference":[{"key":"35_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1007\/978-3-030-58621-8_10","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Ali Athar","year":"2020","unstructured":"Athar, Ali, Mahadevan, Sabarinath, Os\u0306ep, Aljos\u0306a, Leal-Taix\u00e9, Laura, Leibe, Bastian: STEm-Seg: spatio-temporal embeddings for instance segmentation in videos. In: Vedaldi, Andrea, Bischof, Horst, Brox, Thomas, Frahm, Jan-Michael. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 158\u2013177. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58621-8_10"},{"key":"35_CR2","doi-asserted-by":"crossref","unstructured":"Bertasius, G., Torresani, L.: Classifying, segmenting, and tracking object instances in video with mask propagation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9739\u20139748 (2020)","DOI":"10.1109\/CVPR42600.2020.00976"},{"key":"35_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1007\/978-3-030-01258-8_21","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Gedas Bertasius","year":"2018","unstructured":"Bertasius, Gedas, Torresani, Lorenzo, Shi, Jianbo: Object detection in video with spatiotemporal sampling networks. In: Ferrari, Vittorio, Hebert, Martial, Sminchisescu, Cristian, Weiss, Yair (eds.) ECCV 2018. LNCS, vol. 11216, pp. 342\u2013357. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01258-8_21"},{"key":"35_CR4","doi-asserted-by":"crossref","unstructured":"Bolya, D., Zhou, C., Xiao, F., Lee, Y.J.: Yolact: real-time instance segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9157\u20139166 (2019)","DOI":"10.1109\/ICCV.2019.00925"},{"key":"35_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-030-58568-6_1","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Jiale Cao","year":"2020","unstructured":"Cao, Jiale, Anwer, Rao Muhammad, Cholakkal, Hisham, Khan, Fahad Shahbaz, Pang, Yanwei, Shao, Ling: SipMask: spatial information preservation for fast image and video instance segmentation. In: Vedaldi, Andrea, Bischof, Horst, Brox, Thomas, Frahm, Jan-Michael. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 1\u201318. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58568-6_1"},{"key":"35_CR6","doi-asserted-by":"crossref","unstructured":"Chen, H., Sun, K., Tian, Z., Shen, C., Huang, Y., Yan, Y.: Blendmask: top-down meets bottom-up for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8573\u20138581 (2020)","DOI":"10.1109\/CVPR42600.2020.00860"},{"key":"35_CR7","doi-asserted-by":"crossref","unstructured":"Chen, K., et al.: Hybrid task cascade for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4974\u20134983 (2019)","DOI":"10.1109\/CVPR.2019.00511"},{"key":"35_CR8","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607 (2020)"},{"key":"35_CR9","unstructured":"Chen, X., Fan, H., Girshick, R., He, K.: Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297 (2020)"},{"key":"35_CR10","doi-asserted-by":"crossref","unstructured":"Dai, J., et al.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 764\u2013773 (2017)","DOI":"10.1109\/ICCV.2017.89"},{"key":"35_CR11","doi-asserted-by":"crossref","unstructured":"Fang, Y., et al.: Instances as queries. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6910\u20136919 (2021)","DOI":"10.1109\/ICCV48922.2021.00683"},{"key":"35_CR12","doi-asserted-by":"crossref","unstructured":"Fu, Y., Yang, L., Liu, D., Huang, T.S., Shi, H.: Compfeat: comprehensive feature aggregation for video instance segmentation. arXiv preprint arXiv:2012.03400 (2020)","DOI":"10.1609\/aaai.v35i2.16225"},{"key":"35_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"35_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"35_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"35_CR16","doi-asserted-by":"crossref","unstructured":"Huang, Z., Huang, L., Gong, Y., Huang, C., Wang, X.: Mask scoring r-cnn. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6409\u20136418 (2019)","DOI":"10.1109\/CVPR.2019.00657"},{"key":"35_CR17","unstructured":"Hwang, S., Heo, M., Oh, S.W., Kim, S.J.: Video instance segmentation using inter-frame communication transformers. Advances in Neural Information Processing Systems 34 (2021)"},{"key":"35_CR18","unstructured":"Jia, X., De Brabandere, B., Tuytelaars, T., Gool, L.V.: Dynamic filter networks. In: Advances in Neural Information Processing Systems, pp. 667\u2013675 (2016)"},{"key":"35_CR19","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Gao, P., Guo, C., Zhang, Q., Xiang, S., Pan, C.: Video object detection with locally-weighted deformable neighbors. In: Proceedings of the AAAI Conference on Artificial Intelligence (2019)","DOI":"10.1609\/aaai.v33i01.33018529"},{"key":"35_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/978-3-030-58517-4_2","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Zhengkai Jiang","year":"2020","unstructured":"Jiang, Zhengkai, et al.: Learning where to focus for efficient video object detection. In: Vedaldi, Andrea, Bischof, Horst, Brox, Thomas, Frahm, Jan-Michael. (eds.) ECCV 2020. LNCS, vol. 12361, pp. 18\u201334. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58517-4_2"},{"key":"35_CR21","unstructured":"Kalantidis, Y., Sariyildiz, M.B., Pion, N., Weinzaepfel, P., Larlus, D.: Hard negative mixing for contrastive learning. arXiv preprint arXiv:2010.01028 (2020)"},{"key":"35_CR22","unstructured":"Ke, L., Li, X., Danelljan, M., Tai, Y.W., Tang, C.K., Yu, F.: Prototypical cross-attention networks for multiple object tracking and segmentation. In: Advances in Neural Information Processing Systems 34 (2021)"},{"key":"35_CR23","doi-asserted-by":"crossref","unstructured":"Li, M., Li, S., Li, L., Zhang, L.: Spatial feature calibration and temporal fusion for effective one-stage video instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 11215\u201311224 (2021)","DOI":"10.1109\/CVPR46437.2021.01106"},{"key":"35_CR24","doi-asserted-by":"crossref","unstructured":"Lin, H., Wu, R., Liu, S., Lu, J., Jia, J.: Video instance segmentation with a propose-reduce paradigm. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1739\u20131748 (2021)","DOI":"10.1109\/ICCV48922.2021.00176"},{"key":"35_CR25","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"35_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"Tsung-Yi Lin","year":"2014","unstructured":"Lin, Tsung-Yi., Maire, Michael, Belongie, Serge, Hays, James, Perona, Pietro, Ramanan, Deva, Doll\u00e1r, Piotr, Zitnick, C. Lawrence.: Microsoft COCO: common objects in context. In: Fleet, David, Pajdla, Tomas, Schiele, Bernt, Tuytelaars, Tinne (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"35_CR27","doi-asserted-by":"crossref","unstructured":"Liu, D., Cui, Y., Tan, W., Chen, Y.: Sg-net: spatial granularity network for one-stage video instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9816\u20139825 (2021)","DOI":"10.1109\/CVPR46437.2021.00969"},{"key":"35_CR28","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759\u20138768 (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"35_CR29","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(11) (2008)"},{"key":"35_CR30","doi-asserted-by":"crossref","unstructured":"Oksuz, K., Cam, B.C., Akbas, E., Kalkan, S.: Rank & sort loss for object detection and instance segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3009\u20133018 (2021)","DOI":"10.1109\/ICCV48922.2021.00300"},{"key":"35_CR31","doi-asserted-by":"crossref","unstructured":"Pang, J., Qiu, L., Li, X., Chen, H., Li, Q., Darrell, T., Yu, F.: Quasi-dense similarity learning for multiple object tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 164\u2013173 (2021)","DOI":"10.1109\/CVPR46437.2021.00023"},{"key":"35_CR32","first-page":"8026","volume":"32","author":"A Paszke","year":"2019","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Adv. Neural. Inf. Process. Syst. 32, 8026\u20138037 (2019)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"35_CR33","unstructured":"Qi, J., et al.: Occluded video instance segmentation. arXiv preprint arXiv:2102.01558 (2021)"},{"issue":"3","key":"35_CR34","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"35_CR35","unstructured":"Sun, C., Baradel, F., Murphy, K., Schmid, C.: Learning video representations using contrastive bidirectional transformer. arXiv preprint arXiv:1906.05743 (2019)"},{"key":"35_CR36","unstructured":"Tian, Y., Sun, C., Poole, B., Krishnan, D., Schmid, C., Isola, P.: What makes for good views for contrastive learning. arXiv preprint arXiv:2005.10243 (2020)"},{"key":"35_CR37","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H.: Conditional convolutions for instance segmentation. In: European Conference on Computer Vision, pp. 282\u2013298 (2020)","DOI":"10.1007\/978-3-030-58452-8_17"},{"key":"35_CR38","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., He, T.: Fcos: fully convolutional one-stage object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9627\u20139636 (2019)","DOI":"10.1109\/ICCV.2019.00972"},{"key":"35_CR39","doi-asserted-by":"crossref","unstructured":"Voigtlaender, P., Chai, Y., Schroff, F., Adam, H., Leibe, B., Chen, L.C.: Feelvos: fast end-to-end embedding learning for video object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9481\u20139490 (2019)","DOI":"10.1109\/CVPR.2019.00971"},{"key":"35_CR40","doi-asserted-by":"crossref","unstructured":"Wang, T., Xu, N., Chen, K., Lin, W.: End-to-end video instance segmentation via spatial-temporal graph neural networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10797\u201310806 (2021)","DOI":"10.1109\/ICCV48922.2021.01062"},{"key":"35_CR41","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1007\/978-3-030-58523-5_38","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Xinlong Wang","year":"2020","unstructured":"Wang, Xinlong, Kong, Tao, Shen, Chunhua, Jiang, Yuning, Li, Lei: SOLO: segmenting objects by locations. In: Vedaldi, Andrea, Bischof, Horst, Brox, Thomas, Frahm, Jan-Michael. (eds.) ECCV 2020. LNCS, vol. 12363, pp. 649\u2013665. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58523-5_38"},{"key":"35_CR42","unstructured":"Wang, X., Zhang, R., Kong, T., Li, L., Shen, C.: Solov2: dynamic and fast instance segmentation. arXiv preprint arXiv:2003.10152 (2020)"},{"key":"35_CR43","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xu, Z., Shen, H., Cheng, B., Yang, L.: Centermask: single shot instance segmentation with point representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 9313\u20139321 (2020)","DOI":"10.1109\/CVPR42600.2020.00933"},{"key":"35_CR44","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xu, Z., Wang, X., Shen, C., Cheng, B., Shen, H., Xia, H.: End-to-end video instance segmentation with transformers. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 8741\u20138750 (2021)","DOI":"10.1109\/CVPR46437.2021.00863"},{"key":"35_CR45","doi-asserted-by":"crossref","unstructured":"Wu, H., Chen, Y., Wang, N., Zhang, Z.: Sequence level semantics aggregation for video object detection. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 9217\u20139225 (2019)","DOI":"10.1109\/ICCV.2019.00931"},{"key":"35_CR46","doi-asserted-by":"crossref","unstructured":"Wu, J., Cao, J., Song, L., Wang, Y., Yang, M., Yuan, J.: Track to detect and segment: An online multi-object tracker. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 12352\u201312361 (2021)","DOI":"10.1109\/CVPR46437.2021.01217"},{"key":"35_CR47","doi-asserted-by":"crossref","unstructured":"Xie, E., Sun, P., Song, X., Wang, W., Liu, X., Liang, D., Shen, C., Luo, P.: Polarmask: Single shot instance segmentation with polar representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 12193\u201312202 (2020)","DOI":"10.1109\/CVPR42600.2020.01221"},{"key":"35_CR48","unstructured":"Xiong, Y., Ren, M., Urtasun, R.: Loco: Local contrastive representation learning. arXiv preprint arXiv:2008.01342 (2020)"},{"key":"35_CR49","unstructured":"Xu, N., Yang, L., Yang, J., Yue, D., Fan, Y., Liang, Y., Huang, T.S.: Youtube-vis dataset 2021 version. https:\/\/youtube-vos.org\/dataset\/vis (2021)"},{"key":"35_CR50","doi-asserted-by":"crossref","unstructured":"Yang, L., Fan, Y., Xu, N.: Video instance segmentation. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 5188\u20135197 (2019)","DOI":"10.1109\/ICCV.2019.00529"},{"key":"35_CR51","doi-asserted-by":"crossref","unstructured":"Yang, L., Wang, Y., Xiong, X., Yang, J., Katsaggelos, A.K.: Efficient video object segmentation via network modulation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6499\u20136507 (2018)","DOI":"10.1109\/CVPR.2018.00680"},{"key":"35_CR52","doi-asserted-by":"crossref","unstructured":"Yang, S., et al.: Crossover learning for fast online video instance segmentation. arXiv preprint arXiv:2104.05970 (2021)","DOI":"10.1109\/ICCV48922.2021.00794"},{"key":"35_CR53","doi-asserted-by":"crossref","unstructured":"Ying, X., Li, X., Chuah, M.C.: Srnet: Spatial relation network for efficient single-stage instance segmentation in videos. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 347\u2013356 (2021)","DOI":"10.1145\/3474085.3475626"},{"key":"35_CR54","doi-asserted-by":"crossref","unstructured":"Zhu, L., Xu, Z., Yang, Y.: Bidirectional multirate reconstruction for temporal modeling in videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2653\u20132662 (2017)","DOI":"10.1109\/CVPR.2017.147"},{"key":"35_CR55","doi-asserted-by":"crossref","unstructured":"Zhu, X., Wang, Y., Dai, J., Yuan, L., Wei, Y.: Flow-guided feature aggregation for video object detection. In: Proceedings of the IEEE International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.52"}],"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_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T12:55:54Z","timestamp":1709816154000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25069-9_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250682","9783031250699"],"references-count":55,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25069-9_35","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)"}}]}}