{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T19:17:52Z","timestamp":1772911072393,"version":"3.50.1"},"publisher-location":"Cham","reference-count":78,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585679","type":"print"},{"value":"9783030585686","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\/978-3-030-58568-6_46","type":"book-chapter","created":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T14:03:09Z","timestamp":1605189789000},"page":"781-799","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Adversarial Ranking Attack and Defense"],"prefix":"10.1007","author":[{"given":"Mo","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Zhenxing","family":"Niu","sequence":"additional","affiliation":[]},{"given":"Le","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Qilin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Hua","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,13]]},"reference":[{"key":"46_CR1","unstructured":"Athalye, A., Carlini, N.: On the robustness of the CVPR 2018 white-box adversarial example defenses. arXiv preprint arXiv:1804.03286 (2018)"},{"key":"46_CR2","unstructured":"Athalye, A., Carlini, N., Wagner, D.: Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples. arXiv preprint arXiv:1802.00420 (2018)"},{"key":"46_CR3","first-page":"27","volume":"164","author":"T Bui","year":"2017","unstructured":"Bui, T., Ribeiro, L., Ponti, M., Collomosse, J.: Compact descriptors for sketch-based image retrieval using a triplet loss convolutional neural network. CVIU 164, 27\u201337 (2017)","journal-title":"CVIU"},{"key":"46_CR4","unstructured":"Carlini, N., Wagner, D.: Defensive distillation is not robust to adversarial examples. arXiv preprint arXiv:1607.04311 (2016)"},{"key":"46_CR5","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 39\u201357. IEEE (2017)","DOI":"10.1109\/SP.2017.49"},{"key":"46_CR6","first-page":"1109","volume":"11","author":"G Chechik","year":"2010","unstructured":"Chechik, G., Sharma, V., Shalit, U., Bengio, S.: Large scale online learning of image similarity through ranking. JMLR 11, 1109\u20131135 (2010)","journal-title":"JMLR"},{"key":"46_CR7","doi-asserted-by":"crossref","unstructured":"Chen, J., Jordan, M.I.: Boundary attack++: Query-efficient decision-based adversarial attack. arXiv preprint arXiv:1904.02144 (2019)","DOI":"10.1109\/SP40000.2020.00045"},{"key":"46_CR8","doi-asserted-by":"crossref","unstructured":"Chen, P.Y., Sharma, Y., Zhang, H., Yi, J., Hsieh, C.J.: EAD: elastic-net attacks to deep neural networks via adversarial examples. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11302"},{"key":"46_CR9","doi-asserted-by":"crossref","unstructured":"Croce, F., Hein, M.: Sparse and imperceivable adversarial attacks. In: ICCV, pp. 4724\u20134732 (2019)","DOI":"10.1109\/ICCV.2019.00482"},{"key":"46_CR10","doi-asserted-by":"crossref","unstructured":"Dong, Y., Liao, F., Pang, T., Su, H., Zhu, J., Hu, X., Li, J.: Boosting adversarial attacks with momentum. In: CVPR (June 2018)","DOI":"10.1109\/CVPR.2018.00957"},{"key":"46_CR11","doi-asserted-by":"crossref","unstructured":"Dong, Y., Pang, T., Su, H., Zhu, J.: Evading defenses to transferable adversarial examples by translation-invariant attacks. In: CVPR, pp. 4312\u20134321 (2019)","DOI":"10.1109\/CVPR.2019.00444"},{"key":"46_CR12","doi-asserted-by":"crossref","unstructured":"Dong, Y., et al.: Efficient decision-based black-box adversarial attacks on face recognition. In: CVPR, pp. 7714\u20137722 (2019)","DOI":"10.1109\/CVPR.2019.00790"},{"key":"46_CR13","unstructured":"Dong, Y., Su, H., Zhu, J., Bao, F.: Towards interpretable deep neural networks by leveraging adversarial examples. arXiv preprint arXiv:1708.05493 (2017)"},{"key":"46_CR14","doi-asserted-by":"crossref","unstructured":"Dubey, A., van der Maaten, L., Yalniz, Z., Li, Y., Mahajan, D.: Defense against adversarial images using web-scale nearest-neighbor search. In: CVPR, pp. 8767\u20138776 (2019)","DOI":"10.1109\/CVPR.2019.00897"},{"key":"46_CR15","unstructured":"Faghri, F., Fleet, D.J., Kiros, J.R., Fidler, S.: VSE++: Improved visual-semantic embeddings, vol. 2, no. 7, p. 8. arXiv preprint arXiv:1707.05612 (2017)"},{"key":"46_CR16","doi-asserted-by":"crossref","unstructured":"Ganeshan, A., Babu, R.V.: FDA: feature disruptive attack. In: ICCV, pp. 8069\u20138079 (2019)","DOI":"10.1109\/ICCV.2019.00816"},{"key":"46_CR17","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"key":"46_CR18","doi-asserted-by":"crossref","unstructured":"Gopinath, D., Katz, G., Pasareanu, C.S., Barrett, C.: DeepSafe: A data-driven approach for checking adversarial robustness in neural networks. arXiv preprint arXiv:1710.00486 (2017)","DOI":"10.1007\/978-3-030-01090-4_1"},{"key":"46_CR19","doi-asserted-by":"crossref","unstructured":"Goren, G., Kurland, O., Tennenholtz, M., Raiber, F.: Ranking robustness under adversarial document manipulations. In: ACM SIGIR, pp. 395\u2013404. ACM (2018)","DOI":"10.1145\/3209978.3210012"},{"key":"46_CR20","unstructured":"Guo, C., Rana, M., Cisse, M., Van Der Maaten, L.: Countering adversarial images using input transformations. arXiv preprint arXiv:1711.00117 (2017)"},{"key":"46_CR21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (June 2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"46_CR22","unstructured":"He, W., Wei, J., Chen, X., Carlini, N., Song, D.: Adversarial example defense: ensembles of weak defenses are not strong. In: 11th USENIX Workshop on Offensive Technologies, WOOT 2017 (2017)"},{"key":"46_CR23","doi-asserted-by":"crossref","unstructured":"He, X., He, Z., Du, X., Chua, T.S.: Adversarial personalized ranking for recommendation. In: ACM SIGIR, pp. 355\u2013364. ACM (2018)","DOI":"10.1145\/3209978.3209981"},{"key":"46_CR24","unstructured":"Huang, Q., et al.: Intermediate level adversarial attack for enhanced transferability. arXiv preprint arXiv:1811.08458 (2018)"},{"key":"46_CR25","unstructured":"Huang, R., Xu, B., Schuurmans, D., Szepesv\u00e1ri, C.: Learning with a strong adversary. CoRR abs\/1511.03034 (2015). http:\/\/arxiv.org\/abs\/1511.03034"},{"key":"46_CR26","doi-asserted-by":"crossref","unstructured":"Jacob, P., Picard, D., Histace, A., Klein, E.: Metric learning with horde: high-order regularizer for deep embeddings. In: ICCV, pp. 6539\u20136548 (2019)","DOI":"10.1109\/ICCV.2019.00664"},{"key":"46_CR27","doi-asserted-by":"crossref","unstructured":"Joachims, T.: Optimizing search engines using clickthrough data. In: ACM SIGKDD, pp. 133\u2013142. ACM (2002)","DOI":"10.1145\/775047.775067"},{"key":"46_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/978-3-319-63387-9_5","volume-title":"Computer Aided Verification","author":"G Katz","year":"2017","unstructured":"Katz, G., Barrett, C., Dill, D.L., Julian, K., Kochenderfer, M.J.: Reluplex: an efficient SMT solver for verifying deep neural networks. In: Majumdar, R., Kun\u010dak, V. (eds.) CAV 2017. LNCS, vol. 10426, pp. 97\u2013117. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-63387-9_5"},{"key":"46_CR29","unstructured":"Kiros, R., Salakhutdinov, R., Zemel, R.S.: Unifying visual-semantic embeddings with multimodal neural language models. arXiv preprint arXiv:1411.2539 (2014)"},{"key":"46_CR30","unstructured":"Komkov, S., Petiushko, A.: AdvHat: Real-world adversarial attack on ArcFace Face ID system. arXiv preprint arXiv:1908.08705 (2019)"},{"key":"46_CR31","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NeurIPS, pp. 1097\u20131105 (2012)"},{"key":"46_CR32","unstructured":"Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial examples in the physical world. arXiv preprint arXiv:1607.02533 (2016)"},{"key":"46_CR33","unstructured":"Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236 (2016)"},{"issue":"11","key":"46_CR34","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"46_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1007\/978-3-030-01225-0_13","volume-title":"Computer Vision \u2013 ECCV 2018","author":"K-H Lee","year":"2018","unstructured":"Lee, K.-H., Chen, X., Hua, G., Hu, H., He, X.: Stacked cross attention for image-text matching. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 212\u2013228. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01225-0_13"},{"key":"46_CR36","doi-asserted-by":"crossref","unstructured":"Li, J., Ji, R., Liu, H., Hong, X., Gao, Y., Tian, Q.: Universal perturbation attack against image retrieval. In: ICCV, pp. 4899\u20134908 (2019)","DOI":"10.1109\/ICCV.2019.00500"},{"key":"46_CR37","doi-asserted-by":"crossref","unstructured":"Liu, H., et al.: Universal adversarial perturbation via prior driven uncertainty approximation. In: ICCV, pp. 2941\u20132949 (2019)","DOI":"10.1109\/ICCV.2019.00303"},{"issue":"3","key":"46_CR38","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1561\/1500000016","volume":"3","author":"TY Liu","year":"2009","unstructured":"Liu, T.Y., et al.: Learning to rank for information retrieval. Found. Trends\u00ae Inf. Retr. 3(3), 225\u2013331 (2009)","journal-title":"Found. Trends\u00ae Inf. Retr."},{"key":"46_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1007\/978-3-030-01234-2_23","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Liu","year":"2018","unstructured":"Liu, X., Cheng, M., Zhang, H., Hsieh, C.-J.: Towards robust neural networks via random self-ensemble. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 381\u2013397. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_23"},{"key":"46_CR40","unstructured":"Liu, X., Li, Y., Wu, C., Hsieh, C.J.: Adv-BNN: Improved adversarial defense through robust Bayesian neural network. arXiv preprint arXiv:1810.01279 (2018)"},{"key":"46_CR41","unstructured":"Liu, Y., Chen, X., Liu, C., Song, D.: Delving into transferable adversarial examples and black-box attacks. arXiv preprint arXiv:1611.02770 (2016)"},{"key":"46_CR42","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhao, Z., Larson, M.: Who\u2019s afraid of adversarial queries?: the impact of image modifications on content-based image retrieval. In: ICMR, pp. 306\u2013314. ACM (2019)","DOI":"10.1145\/3323873.3325052"},{"key":"46_CR43","doi-asserted-by":"crossref","unstructured":"Lu, J., Issaranon, T., Forsyth, D.: SafetyNet: detecting and rejecting adversarial examples robustly. In: ICCV, pp. 446\u2013454 (2017)","DOI":"10.1109\/ICCV.2017.56"},{"key":"46_CR44","unstructured":"Luo, Y., Boix, X., Roig, G., Poggio, T., Zhao, Q.: Foveation-based mechanisms alleviate adversarial examples. arXiv preprint arXiv:1511.06292 (2015)"},{"key":"46_CR45","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017)"},{"key":"46_CR46","unstructured":"Mao, C., Zhong, Z., Yang, J., Vondrick, C., Ray, B.: Metric learning for adversarial robustness. In: NeurIPS, pp. 478\u2013489 (2019)"},{"key":"46_CR47","doi-asserted-by":"crossref","unstructured":"Meng, D., Chen, H.: MagNet: a two-pronged defense against adversarial examples. In: ACM SIGSAC, pp. 135\u2013147. ACM (2017)","DOI":"10.1145\/3133956.3134057"},{"key":"46_CR48","unstructured":"Metzen, J.H., Genewein, T., Fischer, V., Bischoff, B.: On detecting adversarial perturbations. arXiv preprint arXiv:1702.04267 (2017)"},{"key":"46_CR49","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765\u20131773 (2017)","DOI":"10.1109\/CVPR.2017.17"},{"key":"46_CR50","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.: DeepFool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574\u20132582 (2016)","DOI":"10.1109\/CVPR.2016.282"},{"key":"46_CR51","doi-asserted-by":"crossref","unstructured":"Mummadi, C.K., Brox, T., Metzen, J.H.: Defending against universal perturbations with shared adversarial training. In: ICCV, pp. 4928\u20134937 (2019)","DOI":"10.1109\/ICCV.2019.00503"},{"key":"46_CR52","doi-asserted-by":"crossref","unstructured":"Niu, Z., Zhou, M., Wang, L., Gao, X., Hua, G.: Hierarchical multimodal LSTM for dense visual-semantic embedding. In: ICCV, pp. 1881\u20131889 (2017)","DOI":"10.1109\/ICCV.2017.208"},{"key":"46_CR53","doi-asserted-by":"crossref","unstructured":"Oh Song, H., Xiang, Y., Jegelka, S., Savarese, S.: Deep metric learning via lifted structured feature embedding. In: CVPR, pp. 4004\u20134012 (2016)","DOI":"10.1109\/CVPR.2016.434"},{"key":"46_CR54","unstructured":"Papernot, N., McDaniel, P.: On the effectiveness of defensive distillation. arXiv preprint arXiv:1607.05113 (2016)"},{"key":"46_CR55","unstructured":"Papernot, N., McDaniel, P., Goodfellow, I.: Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. arXiv preprint arXiv:1605.07277 (2016)"},{"key":"46_CR56","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z.B., Swami, A.: Practical black-box attacks against machine learning. In: Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, pp. 506\u2013519. ACM (2017)","DOI":"10.1145\/3052973.3053009"},{"key":"46_CR57","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: 2016 IEEE European Symposium on Security and Privacy (EuroS&P), pp. 372\u2013387. IEEE (2016)","DOI":"10.1109\/EuroSP.2016.36"},{"key":"46_CR58","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P., Wu, X., Jha, S., Swami, A.: Distillation as a defense to adversarial perturbations against deep neural networks. In: 2016 IEEE Symposium on Security and Privacy (SP), pp. 582\u2013597. IEEE (2016)","DOI":"10.1109\/SP.2016.41"},{"key":"46_CR59","unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch (2017)"},{"key":"46_CR60","doi-asserted-by":"crossref","unstructured":"Prakash, A., Moran, N., Garber, S., DiLillo, A., Storer, J.: Deflecting adversarial attacks with pixel deflection. In: CVPR, pp. 8571\u20138580 (2018)","DOI":"10.1109\/CVPR.2018.00894"},{"key":"46_CR61","unstructured":"Sabour, S., Cao, Y., Faghri, F., Fleet, D.J.: Adversarial manipulation of deep representations. arXiv preprint arXiv:1511.05122 (2015)"},{"key":"46_CR62","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: CVPR, pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"46_CR63","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.neucom.2018.04.027","volume":"307","author":"U Shaham","year":"2018","unstructured":"Shaham, U., Yamada, Y., Negahban, S.: Understanding adversarial training: increasing local stability of supervised models through robust optimization. Neurocomputing 307, 195\u2013204 (2018)","journal-title":"Neurocomputing"},{"key":"46_CR64","doi-asserted-by":"crossref","unstructured":"Sharif, M., Bhagavatula, S., Bauer, L., Reiter, M.K.: Accessorize to a crime: real and stealthy attacks on state-of-the-art face recognition. In: ACM SIGSAC, pp. 1528\u20131540. ACM (2016)","DOI":"10.1145\/2976749.2978392"},{"key":"46_CR65","doi-asserted-by":"crossref","unstructured":"Shi, Y., Wang, S., Han, Y.: Curls & Whey: boosting black-box adversarial attacks. arXiv preprint arXiv:1904.01160 (2019)","DOI":"10.1109\/CVPR.2019.00668"},{"key":"46_CR66","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1109\/TEVC.2019.2890858","volume":"23","author":"J Su","year":"2019","unstructured":"Su, J., Vargas, D.V., Sakurai, K.: One pixel attack for fooling deep neural networks. IEEE Trans. Evol. Comput. 23, 828\u2013841 (2019)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"46_CR67","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: CVPR, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"46_CR68","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"46_CR69","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)"},{"key":"46_CR70","doi-asserted-by":"crossref","unstructured":"Wang, J., et al.: Learning fine-grained image similarity with deep ranking. In: CVPR, pp. 1386\u20131393 (2014)","DOI":"10.1109\/CVPR.2014.180"},{"key":"46_CR71","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, H.: Bilateral adversarial training: towards fast training of more robust models against adversarial attacks. In: ICCV, pp. 6629\u20136638 (2019)","DOI":"10.1109\/ICCV.2019.00673"},{"key":"46_CR72","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zheng, S., Song, M., Wang, Q., Rahimpour, A., Qi, H.: advPattern: physical-world attacks on deep person re-identification via adversarially transformable patterns. In: ICCV, pp. 8341\u20138350 (2019)","DOI":"10.1109\/ICCV.2019.00843"},{"key":"46_CR73","unstructured":"Wu, L., Zhu, Z., Tai, C., et al.: Understanding and enhancing the transferability of adversarial examples. arXiv preprint arXiv:1802.09707 (2018)"},{"key":"46_CR74","unstructured":"Xiao, C., Zhu, J.Y., Li, B., He, W., Liu, M., Song, D.: Spatially transformed adversarial examples. arXiv preprint arXiv:1801.02612 (2018)"},{"key":"46_CR75","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)"},{"key":"46_CR76","doi-asserted-by":"crossref","unstructured":"Xie, C., et al.: Improving transferability of adversarial examples with input diversity. In: CVPR, pp. 2730\u20132739 (2019)","DOI":"10.1109\/CVPR.2019.00284"},{"key":"46_CR77","first-page":"2805","volume":"30","author":"X Yuan","year":"2019","unstructured":"Yuan, X., He, P., Zhu, Q., Li, X.: Adversarial examples: attacks and defenses for deep learning. IEEE TNNLS 30, 2805\u20132824 (2019)","journal-title":"IEEE TNNLS"},{"key":"46_CR78","doi-asserted-by":"crossref","unstructured":"Zhong, Y., Deng, W.: Adversarial learning with margin-based triplet embedding regularization. In: ICCV, pp. 6549\u20136558 (2019)","DOI":"10.1109\/ICCV.2019.00665"}],"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\/978-3-030-58568-6_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:28:24Z","timestamp":1731371304000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58568-6_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585679","9783030585686"],"references-count":78,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58568-6_46","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":"13 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. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","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)"}}]}}