{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T17:39:36Z","timestamp":1770917976075,"version":"3.50.1"},"publisher-location":"Cham","reference-count":62,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031198359","type":"print"},{"value":"9783031198366","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-19836-6_24","type":"book-chapter","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T09:04:58Z","timestamp":1666343098000},"page":"422-439","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Relationformer: A Unified Framework for\u00a0Image-to-Graph Generation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4435-7207","authenticated-orcid":false,"given":"Suprosanna","family":"Shit","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3441-8192","authenticated-orcid":false,"given":"Rajat","family":"Koner","sequence":"additional","affiliation":[]},{"given":"Bastian","family":"Wittmann","sequence":"additional","affiliation":[]},{"given":"Johannes","family":"Paetzold","sequence":"additional","affiliation":[]},{"given":"Ivan","family":"Ezhov","sequence":"additional","affiliation":[]},{"given":"Hongwei","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jiazhen","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Sahand","family":"Sharifzadeh","sequence":"additional","affiliation":[]},{"given":"Georgios","family":"Kaissis","sequence":"additional","affiliation":[]},{"given":"Volker","family":"Tresp","sequence":"additional","affiliation":[]},{"given":"Bjoern","family":"Menze","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,22]]},"reference":[{"key":"24_CR1","doi-asserted-by":"crossref","unstructured":"Armeni, I., et al.: 3D scene graph: a structure for unified semantics, 3D space, and camera. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5664\u20135673 (2019)","DOI":"10.1109\/ICCV.2019.00576"},{"key":"24_CR2","unstructured":"Ba, J.L., et al.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)"},{"key":"24_CR3","doi-asserted-by":"crossref","unstructured":"Bastani, F., et al.: RoadTracer: automatic extraction of road networks from aerial images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4720\u20134728 (2018)","DOI":"10.1109\/CVPR.2018.00496"},{"key":"24_CR4","doi-asserted-by":"crossref","unstructured":"Batra, A.: Improved road connectivity by joint learning of orientation and segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10385\u201310393 (2019)","DOI":"10.1109\/CVPR.2019.01063"},{"key":"24_CR5","unstructured":"Belli, D., Kipf, T.: Image-conditioned graph generation for road network extraction. arXiv preprint arXiv:1910.14388 (2019)"},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Bello, I., et al.: Attention augmented convolutional networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3286\u20133295 (2019)","DOI":"10.1109\/ICCV.2019.00338"},{"key":"24_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"N Carion","year":"2020","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213\u2013229. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13"},{"key":"24_CR8","doi-asserted-by":"crossref","unstructured":"Chen, T., et al.: Knowledge-embedded routing network for scene graph generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6163\u20136171 (2019)","DOI":"10.1109\/CVPR.2019.00632"},{"key":"24_CR9","doi-asserted-by":"crossref","unstructured":"Chu, H., et al.: Neural turtle graphics for modeling city road layouts. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4522\u20134530 (2019)","DOI":"10.1109\/ICCV.2019.00462"},{"key":"24_CR10","unstructured":"Cong, Y., et al.: RelTR: relation transformer for scene graph generation. arXiv preprint arXiv:2201.11460 (2022)"},{"key":"24_CR11","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":"24_CR12","doi-asserted-by":"crossref","unstructured":"Dhingra, N., Ritter, F., Kunz, A.: BGT-Net: bidirectional GRU transformer network for scene graph generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2150\u20132159 (2021)","DOI":"10.1109\/CVPRW53098.2021.00244"},{"key":"24_CR13","unstructured":"Dosovitskiy, A., et al.: An image is worth $$16\\times 16$$ words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"Drees, D., Scherzinger, A., H\u00e4gerling, R., Kiefer, F., Jiang, X.: Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets. arXiv preprint arXiv:2102.03444 (2021)","DOI":"10.1186\/s12859-021-04262-w"},{"key":"24_CR15","unstructured":"Fang, Y., et al.: You only look at one sequence: rethinking transformer in vision through object detection. arXiv preprint arXiv:2106.00666 (2021)"},{"key":"24_CR16","unstructured":"Hamilton, W.L., et al.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025\u20131035 (2017)"},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"He, K., et al.: 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":"24_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/978-3-030-58586-0_4","volume-title":"Computer Vision \u2013 ECCV 2020","author":"S He","year":"2020","unstructured":"He, S., et al.: Sat2Graph: road graph extraction through graph-tensor encoding. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12369, pp. 51\u201367. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58586-0_4"},{"key":"24_CR19","unstructured":"Hildebrandt, M., et al.: Scene graph reasoning for visual question answering. arXiv preprint arXiv:2007.01072 (2020)"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Ji, J., et al.: Action genome: actions as compositions of spatio-temporal scene graphs. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10236\u201310247 (2020)","DOI":"10.1109\/CVPR42600.2020.01025"},{"issue":"7","key":"24_CR21","doi-asserted-by":"publisher","first-page":"1168","DOI":"10.1016\/j.neuron.2021.02.006","volume":"109","author":"X Ji","year":"2021","unstructured":"Ji, X., et al.: Brain microvasculature has a common topology with local differences in geometry that match metabolic load. Neuron 109(7), 1168\u20131187 (2021)","journal-title":"Neuron"},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Johnson, J., et al.: Image retrieval using scene graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3668\u20133678 (2015)","DOI":"10.1109\/CVPR.2015.7298990"},{"key":"24_CR23","unstructured":"Koner, R., Sinhamahapatra, P., Roscher, K., G\u00fcnnemann, S., Tresp, V.: OODformer: out-of-distribution detection transformer. arXiv preprint arXiv:2107.08976 (2021)"},{"key":"24_CR24","unstructured":"Koner, R., et al.: Relation transformer network. arXiv preprint arXiv:2004.06193 (2020)"},{"key":"24_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/978-3-030-88361-4_7","volume-title":"The Semantic Web \u2013 ISWC 2021","author":"R Koner","year":"2021","unstructured":"Koner, R., Li, H., Hildebrandt, M., Das, D., Tresp, V., G\u00fcnnemann, S.: Graphhopper: multi-hop scene graph reasoning for visual question answering. In: Hotho, A., et al. (eds.) ISWC 2021. LNCS, vol. 12922, pp. 111\u2013127. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-88361-4_7"},{"key":"24_CR26","unstructured":"Koner, R., et al.: Scenes and surroundings: scene graph generation using relation transformer. arXiv preprint arXiv:2107.05448 (2021)"},{"key":"24_CR27","unstructured":"Krishna, R., et al.: Visual genome: connecting language and vision using crowdsourced dense image annotations. arXiv preprint arXiv:1602.07332 (2016)"},{"key":"24_CR28","doi-asserted-by":"crossref","unstructured":"Li, R., et al.: Bipartite graph network with adaptive message passing for unbiased scene graph generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11109\u201311119 (2021)","DOI":"10.1109\/CVPR46437.2021.01096"},{"key":"24_CR29","doi-asserted-by":"crossref","unstructured":"Li, R., et al.: SGTR: end-to-end scene graph generation with transformer. arXiv preprint arXiv:2112.12970 (2021)","DOI":"10.1109\/CVPR52688.2022.01888"},{"key":"24_CR30","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":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"24_CR31","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., et al.: 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":"24_CR32","doi-asserted-by":"crossref","unstructured":"Lin, X., et al.: GPS-Net: graph property sensing network for scene graph generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3746\u20133753 (2020)","DOI":"10.1109\/CVPR42600.2020.00380"},{"key":"24_CR33","doi-asserted-by":"crossref","unstructured":"Liu, H., et al.: Fully convolutional scene graph generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11546\u201311556 (2021)","DOI":"10.1109\/CVPR46437.2021.01138"},{"key":"24_CR34","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. arXiv preprint arXiv:2103.14030 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"24_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1007\/978-3-319-46448-0_51","volume-title":"Computer Vision \u2013 ECCV 2016","author":"C Lu","year":"2016","unstructured":"Lu, C., Krishna, R., Bernstein, M., Fei-Fei, L.: Visual relationship detection with language priors. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 852\u2013869. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_51"},{"key":"24_CR36","doi-asserted-by":"crossref","unstructured":"Lu, Y., et al.: Context-aware scene graph generation with Seq2Seq transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15931\u201315941 (2021)","DOI":"10.1109\/ICCV48922.2021.01563"},{"key":"24_CR37","doi-asserted-by":"crossref","unstructured":"M\u00e1ttyus, G., et al.: DeepRoadMapper: extracting road topology from aerial images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3438\u20133446 (2017)","DOI":"10.1109\/ICCV.2017.372"},{"issue":"6","key":"24_CR38","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1109\/MCG.2009.130","volume":"29","author":"J Meyer-Spradow","year":"2009","unstructured":"Meyer-Spradow, J., et al.: Voreen: a rapid-prototyping environment for ray-casting-based volume visualizations. IEEE Comput. Graph. Appl. 29(6), 6\u201313 (2009)","journal-title":"IEEE Comput. Graph. Appl."},{"key":"24_CR39","doi-asserted-by":"crossref","unstructured":"Miettinen, A., et al.: Micrometer-resolution reconstruction and analysis of whole mouse brain vasculature by synchrotron-based phase-contrast tomographic microscopy. BioRxiv (2021)","DOI":"10.1101\/2021.03.16.435616"},{"key":"24_CR40","unstructured":"Newell, A., Deng, J.: Pixels to graphs by associative embedding. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"24_CR41","unstructured":"Paetzold, J.C., et al.: Whole brain vessel graphs: a dataset and benchmark for graph learning and neuroscience. In: Thirty-Fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2) (2021)"},{"key":"24_CR42","doi-asserted-by":"crossref","unstructured":"Pennington, J., et al.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"24_CR43","unstructured":"Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"24_CR44","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/978-3-030-58604-1_25","volume-title":"Computer Vision \u2013 ECCV 2020","author":"M Rol\u00ednek","year":"2020","unstructured":"Rol\u00ednek, M., Swoboda, P., Zietlow, D., Paulus, A., Musil, V., Martius, G.: Deep graph matching via blackbox differentiation of combinatorial solvers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12373, pp. 407\u2013424. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58604-1_25"},{"key":"24_CR45","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"24_CR46","doi-asserted-by":"crossref","unstructured":"Sharifzadeh, S., et al.: Classification by attention: scene graph classification with prior knowledge. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 5025\u20135033 (2021)","DOI":"10.1609\/aaai.v35i6.16636"},{"key":"24_CR47","unstructured":"Sharifzadeh, S., et al.: Improving scene graph classification by exploiting knowledge from texts. arXiv preprint arXiv:2102.04760 (2021)"},{"key":"24_CR48","doi-asserted-by":"crossref","unstructured":"Sharifzadeh, S., et al.: Improving visual relation detection using depth maps. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 3597\u20133604. IEEE (2021)","DOI":"10.1109\/ICPR48806.2021.9412945"},{"key":"24_CR49","doi-asserted-by":"crossref","unstructured":"Shit, S., et al.: clDice-a novel topology-preserving loss function for tubular structure segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16560\u201316569 (2021)","DOI":"10.1109\/CVPR46437.2021.01629"},{"key":"24_CR50","unstructured":"Song, H., et al.: ViDT: an efficient and effective fully transformer-based object detector. arXiv preprint arXiv:2110.03921 (2021)"},{"key":"24_CR51","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.3389\/fnins.2020.592352","volume":"14","author":"G Tetteh","year":"2020","unstructured":"Tetteh, G., et al.: DeepVesselNet: vessel segmentation, centerline prediction, and bifurcation detection in 3-D angiographic volumes. Front. Neurosci. 14, 1285 (2020)","journal-title":"Front. Neurosci."},{"issue":"4","key":"24_CR52","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1038\/s41592-020-0792-1","volume":"17","author":"MI Todorov","year":"2020","unstructured":"Todorov, M.I., et al.: Machine learning analysis of whole mouse brain vasculature. Nat. Methods 17(4), 442\u2013449 (2020)","journal-title":"Nat. Methods"},{"key":"24_CR53","doi-asserted-by":"crossref","unstructured":"Touvron, H., et al.: Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning, pp. 10347\u201310357. PMLR (2021)","DOI":"10.1109\/ICCV48922.2021.00010"},{"key":"24_CR54","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"24_CR55","doi-asserted-by":"crossref","unstructured":"Xu, D., et al.: Scene graph generation by iterative message passing. In: Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.330"},{"key":"24_CR56","doi-asserted-by":"crossref","unstructured":"Xu, D., et al.: Scene graph generation by iterative message passing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5410\u20135419 (2017)","DOI":"10.1109\/CVPR.2017.330"},{"key":"24_CR57","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1007\/978-3-030-01246-5_41","volume-title":"Computer Vision \u2013 ECCV 2018","author":"J Yang","year":"2018","unstructured":"Yang, J., Lu, J., Lee, S., Batra, D., Parikh, D.: Graph R-CNN for scene graph generation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 690\u2013706. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01246-5_41"},{"key":"24_CR58","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1007\/978-3-030-58592-1_36","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Zareian","year":"2020","unstructured":"Zareian, A., Karaman, S., Chang, S.-F.: Bridging knowledge graphs to generate scene graphs. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12368, pp. 606\u2013623. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58592-1_36"},{"key":"24_CR59","doi-asserted-by":"crossref","unstructured":"Zellers, R., et al.: Neural motifs: scene graph parsing with global context. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5831\u20135840 (2018)","DOI":"10.1109\/CVPR.2018.00611"},{"key":"24_CR60","unstructured":"Zhang, M., Chen, Y.: Link prediction based on graph neural networks (2018)"},{"key":"24_CR61","unstructured":"Zhou, X., et al.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)"},{"key":"24_CR62","unstructured":"Zhu, X., et al.: Deformable DETR: deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 (2020)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19836-6_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T08:43:47Z","timestamp":1728204227000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19836-6_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198359","9783031198366"],"references-count":62,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19836-6_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"22 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1645","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.21","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.91","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}