{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T04:53:54Z","timestamp":1781585634050,"version":"3.54.5"},"publisher-location":"Cham","reference-count":66,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030586034","type":"print"},{"value":"9783030586041","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\/978-3-030-58604-1_25","type":"book-chapter","created":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T22:02:49Z","timestamp":1604354569000},"page":"407-424","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers"],"prefix":"10.1007","author":[{"given":"Michal","family":"Rol\u00ednek","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Swoboda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dominik","family":"Zietlow","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anselm","family":"Paulus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V\u00edt","family":"Musil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georg","family":"Martius","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,11,3]]},"reference":[{"key":"25_CR1","unstructured":"Adams, R.P., Zemel, R.S.: Ranking via sinkhorn propagation (2011)"},{"key":"25_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/978-3-319-24947-6_23","volume-title":"Pattern Recognition","author":"H Abu Alhaija","year":"2015","unstructured":"Abu Alhaija, H., Sellent, A., Kondermann, D., Rother, C.: GraphFlow \u2013 6D large displacement scene flow via graph matching. In: Gall, J., Gehler, P., Leibe, B. (eds.) GCPR 2015. LNCS, vol. 9358, pp. 285\u2013296. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24947-6_23"},{"key":"25_CR3","unstructured":"Amos, B., Kolter, J.Z.: OptNet: differentiable optimization as a layer in neural networks. In: International Conference on Machine Learning. ICML 2017, pp. 136\u2013145 (2017)"},{"issue":"1","key":"25_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11263-010-0390-2","volume":"92","author":"S Baker","year":"2011","unstructured":"Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. Int. J. Comput. Vision 92(1), 1\u201331 (2011). https:\/\/doi.org\/10.1007\/s11263-010-0390-2","journal-title":"Int. J. Comput. Vision"},{"key":"25_CR5","unstructured":"Balcan, M., Dick, T., Sandholm, T., Vitercik, E.: Learning to branch. In: International Conference on Machine Learning. ICML 2018, pp. 353\u2013362 (2018)"},{"key":"25_CR6","unstructured":"Battaglia, P., et al.: Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261 (2018)"},{"key":"25_CR7","unstructured":"Bello, I., Pham, H., Le, Q.V., Norouzi, M., Bengio, S.: Neural combinatorial optimization with reinforcement learning. In: International Conference on Learning Representations, Workshop Track. ICLR 2017 (2017)"},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"Bourdev, L., Malik, J.: Poselets: Body part detectors trained using 3D human pose annotations. In: IEEE International Conference on Computer Vision. ICCV 2009, pp. 1365\u20131372 (2009)","DOI":"10.1109\/ICCV.2009.5459303"},{"key":"25_CR9","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898717754","volume-title":"Assignment Problems","author":"R Burkard","year":"2009","unstructured":"Burkard, R., Dell\u2019Amico, M., Martello, S.: Assignment Problems. Society for Industrial and Applied Mathematics, Philadelphia (2009)"},{"issue":"4","key":"25_CR10","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1023\/A:1008293323270","volume":"10","author":"RE Burkard","year":"1997","unstructured":"Burkard, R.E., Karisch, S.E., Rendl, F.: QAPLIB-a quadratic assignment problem library. J. Global Optim. 10(4), 391\u2013403 (1997). https:\/\/doi.org\/10.1023\/A:1008293323270","journal-title":"J. Global Optim."},{"key":"25_CR11","doi-asserted-by":"crossref","unstructured":"Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2017 (2017)","DOI":"10.1109\/CVPR.2017.143"},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"Chang, J.R., Chen, Y.S.: Pyramid stereo matching network. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2018, pp. 5410\u20135418 (2018)","DOI":"10.1109\/CVPR.2018.00567"},{"key":"25_CR13","unstructured":"Chen, H.T., Lin, H.H., Liu, T.L.: Multi-object tracking using dynamical graph matching. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 2, pp. II-II. IEEE (2001)"},{"key":"25_CR14","doi-asserted-by":"crossref","unstructured":"Cho, M., Alahari, K., Ponce, J.: Learning graphs to match. In: IEEE International Conference on Computer Vision. ICCV 2013 (2013)","DOI":"10.1109\/ICCV.2013.11"},{"key":"25_CR15","first-page":"793","volume":"7","author":"B Delaunay","year":"1934","unstructured":"Delaunay, B.: Sur la sphere vide. Izv. Akad. Nauk SSSR Otdelenie Matematicheskii i Estestvennyka Nauk 7, 793\u2013800 (1934)","journal-title":"Izv. Akad. Nauk SSSR Otdelenie Matematicheskii i Estestvennyka Nauk"},{"key":"25_CR16","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2009, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"25_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1007\/978-3-319-93031-2_12","volume-title":"Integration of Constraint Programming, Artificial Intelligence, and Operations Research","author":"M Deudon","year":"2018","unstructured":"Deudon, M., Cournut, P., Lacoste, A., Adulyasak, Y., Rousseau, L.-M.: Learning heuristics for the TSP by policy gradient. In: van Hoeve, W.-J. (ed.) CPAIOR 2018. LNCS, vol. 10848, pp. 170\u2013181. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93031-2_12"},{"key":"25_CR18","doi-asserted-by":"crossref","unstructured":"Duchenne, O., Joulin, A., Ponce, J.: A graph-matching kernel for object categorization. In: 2011 International Conference on Computer Vision, pp. 1792\u20131799. IEEE (2011)","DOI":"10.1109\/ICCV.2011.6126445"},{"issue":"4","key":"25_CR19","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1109\/TCBB.2015.2474391","volume":"13","author":"A Elmsallati","year":"2016","unstructured":"Elmsallati, A., Clark, C., Kalita, J.: Global alignment of protein-protein interaction networks: a survey. IEEE\/ACM Trans. Comput. Biol. Bioinform. 13(4), 689\u2013705 (2016)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"issue":"2","key":"25_CR20","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vision 88(2), 303\u2013338 (2010). https:\/\/doi.org\/10.1007\/s11263-009-0275-4","journal-title":"Int. J. Comput. Vision"},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Ferber, A., Wilder, B., Dilkina, B., Tambe, M.: Mipaal: Mixed integer program as a layer. arXiv preprint arXiv:1907.05912 (2019)","DOI":"10.1609\/aaai.v34i02.5509"},{"key":"25_CR22","doi-asserted-by":"crossref","unstructured":"Fey, M., Eric Lenssen, J., Weichert, F., M\u00fcller, H.: SplineCNN: fast geometric deep learning with continuous b-spline kernels. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2018, pp. 869\u2013877 (2018)","DOI":"10.1109\/CVPR.2018.00097"},{"key":"25_CR23","unstructured":"Fey, M., Lenssen, J.E., Morris, C., Masci, J., Kriege, N.M.: Deep graph matching consensus. In: International Conference on Learning Representations. ICLR 2020 (2020)"},{"key":"25_CR24","unstructured":"Fey, M., Lenssen, J.E., Morris, C., Masci, J., Kriege, N.M.: Deep graph matching consensus. https:\/\/github.com\/rusty1s\/deep-graph-matching-consensus (2020). Commit: be1c4c"},{"key":"25_CR25","unstructured":"Gasse, M., Ch\u00e9telat, D., Ferroni, N., Charlin, L., Lodi, A.: Exact combinatorial optimization with graph convolutional neural networks. In: Advances in Neural Information Processing Systems. NIPS 2019, pp. 15554\u201315566 (2019)"},{"key":"25_CR26","unstructured":"Grohe, M., Rattan, G., Woeginger, G.J.: Graph similarity and approximate isomorphism. In: 43rd International Symposium on Mathematical Foundations of Computer Science (MFCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), vol. 117, pp. 20:1\u201320:16 (2018)"},{"key":"25_CR27","unstructured":"Jiang, B., Sun, P., Tang, J., Luo, B.: GLMNet: graph learning-matching networks for feature matching. arXiv preprint arXiv:1911.07681 (2019)"},{"key":"25_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/978-3-319-10404-1_11","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2014","author":"D Kainmueller","year":"2014","unstructured":"Kainmueller, D., Jug, F., Rother, C., Myers, G.: Active graph matching for automatic joint segmentation and annotation of C. elegans. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8673, pp. 81\u201388. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10404-1_11"},{"key":"25_CR29","unstructured":"Khalil, E., Dai, H., Zhang, Y., Dilkina, B., Song, L.: Learning combinatorial optimization algorithms over graphs. In: Advances in Neural Information Processing Systems. NIPS 2017, pp. 6348\u20136358 (2017)"},{"key":"25_CR30","doi-asserted-by":"crossref","unstructured":"Khalil, E.B., Bodic, P.L., Song, L., Nemhauser, G., Dilkina, B.: Learning to branch in mixed integer programming. In: AAAI Conference on Artificial Intelligence. AAAI 2016, pp. 724\u2013731 (2016)","DOI":"10.1609\/aaai.v30i1.10080"},{"key":"25_CR31","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations. ICLR 2014 (2014)"},{"key":"25_CR32","unstructured":"Kool, W., van Hoof, H., Welling, M.: Attention, learn to solve routing problems! In: International Conference on Learning Representations. ICLR 2019 (2019)"},{"issue":"4","key":"25_CR33","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1287\/mnsc.9.4.586","volume":"9","author":"EL Lawler","year":"1963","unstructured":"Lawler, E.L.: The quadratic assignment problem. Manag. Sci. 9(4), 586\u2013599 (1963)","journal-title":"Manag. Sci."},{"key":"25_CR34","unstructured":"Li, Y., Zemel, R., Brockschmidt, M., Tarlow, D.: Gated graph sequence neural networks. In: International Conference on Learning Representations. ICLR 2016 (2016)"},{"key":"25_CR35","unstructured":"Liu, L., Cheung, W.K., Li, X., Liao, L.: Aligning users across social networks using network embedding. In: International Joint Conference on Artificial Intelligence. IJCAI 2016, pp. 1774\u20131780 (2016)"},{"key":"25_CR36","doi-asserted-by":"crossref","unstructured":"Luo, W., Schwing, A.G., Urtasun, R.: Efficient deep learning for stereo matching. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2016, pp. 5695\u20135703 (2016)","DOI":"10.1109\/CVPR.2016.614"},{"key":"25_CR37","unstructured":"Mandi, J., Demirovic, E., Stuckey, P.J., Guns, T.: Smart predict-and-optimize for hard combinatorial optimization problems. arXiv preprint arXiv:1911.10092 (2019)"},{"key":"25_CR38","unstructured":"Min, J., Lee, J., Ponce, J., Cho, M.: SPair-71k: a large-scale benchmark for semantic correspondance. arXiv preprint arXiv:1908.10543 (2019)"},{"key":"25_CR39","doi-asserted-by":"crossref","unstructured":"Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. arXiv preprint arXiv:1510.07945 (2015)","DOI":"10.1109\/CVPR.2016.465"},{"key":"25_CR40","unstructured":"Niculae, V., Martins, A., Blondel, M., Cardie, C.: SparseMAP: differentiable sparse structured inference. In: International Conference on Machine Learning. ICML 2018, pp. 3799\u20133808 (2018)"},{"key":"25_CR41","unstructured":"Pachauri, D., Kondor, R., Singh, V.: Solving the multi-way matching problem by permutation synchronization. In: Advances in Neural Information Processing Systems. NIPS 2013, pp. 1860\u20131868 (2013)"},{"key":"25_CR42","doi-asserted-by":"crossref","unstructured":"Rol\u00ednek, M., Musil, V., Paulus, A., Vlastelica, M., Michaelis, C., Martius, G.: Optimizing ranking-based metrics with blackbox differentiation. In: Conference on Computer Vision and Pattern Recognition. CVPR 2020, pp. 7620\u20137630 (2020)","DOI":"10.1109\/CVPR42600.2020.00764"},{"issue":"8","key":"25_CR43","doi-asserted-by":"publisher","first-page":"1705","DOI":"10.1007\/s00371-019-01760-0","volume":"36","author":"Y Sahillio\u011flu","year":"2019","unstructured":"Sahillio\u011flu, Y.: Recent advances in shape correspondence. Vis. Comput. 36(8), 1705\u20131721 (2019). https:\/\/doi.org\/10.1007\/s00371-019-01760-0","journal-title":"Vis. Comput."},{"issue":"1","key":"25_CR44","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. Trans. Neur. Netw. 20(1), 61\u201380 (2009)","journal-title":"Trans. Neur. Netw."},{"key":"25_CR45","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2015, pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"25_CR46","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"25_CR47","doi-asserted-by":"publisher","first-page":"343","DOI":"10.2140\/pjm.1967.21.343","volume":"21","author":"R Sinkhorn","year":"1967","unstructured":"Sinkhorn, R., Knopp, P.: Concerning nonnegative matrices and doubly stochastic matrices. Pac. J. Math. 21, 343\u2013348 (1967)","journal-title":"Pac. J. Math."},{"issue":"3","key":"25_CR48","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1109\/83.826783","volume":"9","author":"G Storvik","year":"2000","unstructured":"Storvik, G., Dahl, G.: Lagrangian-based methods for finding MAP solutions for MRF models. IEEE Trans. Image Process. 9(3), 469\u2013479 (2000)","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"25_CR49","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s11263-013-0644-x","volume":"106","author":"D Sun","year":"2014","unstructured":"Sun, D., Roth, S., Black, M.J.: A quantitative analysis of current practices in optical flow estimation and the principles behind them. Int. J. Comput. Vis. 106(2), 115\u2013137 (2014). https:\/\/doi.org\/10.1007\/s11263-013-0644-x","journal-title":"Int. J. Comput. Vis."},{"key":"25_CR50","doi-asserted-by":"crossref","unstructured":"Sun, D., Yang, X., Liu, M.Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2018, June 2018","DOI":"10.1109\/CVPR.2018.00931"},{"key":"25_CR51","doi-asserted-by":"crossref","unstructured":"Swoboda, P., Kuske, J., Savchynskyy, B.: A dual ascent framework for Lagrangean decomposition of combinatorial problems. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2017, pp. 1596\u20131606 (2017)","DOI":"10.1109\/CVPR.2017.526"},{"key":"25_CR52","doi-asserted-by":"crossref","unstructured":"Swoboda, P., Mokarian, A., Theobalt, C., Bernard, F., et al.: A convex relaxation for multi-graph matching. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2019, pp. 11156\u201311165 (2019)","DOI":"10.1109\/CVPR.2019.01141"},{"key":"25_CR53","doi-asserted-by":"crossref","unstructured":"Swoboda, P., Rother, C., Alhaija, H.A., Kainm\u00fcller, D., Savchynskyy, B.: A study of Lagrangean decompositions and dual ascent solvers for graph matching. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2016, pp. 7062\u20137071 (2016)","DOI":"10.1109\/CVPR.2017.747"},{"issue":"2","key":"25_CR54","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1109\/TPAMI.2012.105","volume":"35","author":"L Torresani","year":"2013","unstructured":"Torresani, L., Kolmogorov, V., Rother, C.: A dual decomposition approach to feature correspondence. IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 259\u2013271 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR55","doi-asserted-by":"crossref","unstructured":"Ufer, N., Ommer, B.: Deep semantic feature matching. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2017, pp. 6914\u20136923 (2017)","DOI":"10.1109\/CVPR.2017.628"},{"key":"25_CR56","unstructured":"Vlastelica, M., Paulus, A., Musil, V., Martius, G., Rol\u00ednek, M.: Differentiation of blackbox combinatorial solvers. In: International Conference on Learning Representations. ICLR 2020 (2020)"},{"key":"25_CR57","doi-asserted-by":"crossref","unstructured":"Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: IEEE International Conference on Computer Vision. ICCV 2015, pp. 3119\u20133127 (2015)","DOI":"10.1109\/ICCV.2015.357"},{"key":"25_CR58","unstructured":"Wang, P.W., Donti, P., Wilder, B., Kolter, Z.: SATNet: bridging deep learning and logical reasoning using a differentiable satisfiability solver. In: International Conference on Machine Learning, pp. 6545\u20136554 (2019)"},{"key":"25_CR59","doi-asserted-by":"crossref","unstructured":"Wang, R., Yan, J., Yang, X.: Learning combinatorial embedding networks for deep graph matching. In: IEEE International Conference on Computer Vision. ICCV 2019, pp. 3056\u20133065 (2019)","DOI":"10.1109\/ICCV.2019.00315"},{"key":"25_CR60","unstructured":"Wang, R., Yan, J., Yang, X.: Neural graph matching network: learning Lawler\u2019s quadratic assignment problem with extension to hypergraph and multiple-graph matching. arXiv preprint arXiv:1911.11308 (2019)"},{"key":"25_CR61","unstructured":"Yu, T., Wang, R., Yan, J., Li, B.: Learning deep graph matching with channel-independent embedding and Hungarian attention. In: International Conference on Learning Representations. ICLR 2020 (2020)"},{"key":"25_CR62","doi-asserted-by":"crossref","unstructured":"Zanfir, A., Sminchisescu, C.: Deep learning of graph matching. In: Conference on Computer Vision and Pattern Recognition. CVPR 2018, pp. 2684\u20132693 (2018)","DOI":"10.1109\/CVPR.2018.00284"},{"key":"25_CR63","unstructured":"Zhang, Y., Hare, J., Pr\u00fcgel-Bennett, A.: Learning representations of sets through optimized permutations. arXiv preprint arXiv:1812.03928 (2018)"},{"key":"25_CR64","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Lee, W.S.: Deep graphical feature learning for the feature matching problem. In: IEEE International Conference on Computer Vision. ICCV 2019 (2019)","DOI":"10.1109\/ICCV.2019.00519"},{"key":"25_CR65","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Shi, Q., McAuley, J., Wei, W., Zhang, Y., van den Hengel, A.: Pairwise matching through max-weight bipartite belief propagation. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2016 (2016)","DOI":"10.1109\/CVPR.2016.135"},{"key":"25_CR66","unstructured":"Zhou, F., la Torre, F.D.: Factorized graph matching. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2012, pp. 127\u2013134 (2012)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58604-1_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T00:15:55Z","timestamp":1730506555000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58604-1_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030586034","9783030586041"],"references-count":66,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58604-1_25","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":"3 November 2020","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":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","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":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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":"7","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":"The conference was held virtually due to the COVID-19 pandemic.","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)"}}]}}