{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T17:32:09Z","timestamp":1785087129042,"version":"3.55.0"},"publisher-location":"Cham","reference-count":67,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031198083","type":"print"},{"value":"9783031198090","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\/978-3-031-19809-0_36","type":"book-chapter","created":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T07:03:04Z","timestamp":1667199784000},"page":"631-648","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":404,"title":["DualPrompt: Complementary Prompting for\u00a0Rehearsal-Free Continual Learning"],"prefix":"10.1007","author":[{"given":"Zifeng","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zizhao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sayna","family":"Ebrahimi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruoxi","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen-Yu","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqi","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guolong","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincent","family":"Perot","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"Dy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomas","family":"Pfister","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,1]]},"reference":[{"key":"36_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1007\/978-3-030-01219-9_9","volume-title":"Computer Vision \u2013 ECCV 2018","author":"R Aljundi","year":"2018","unstructured":"Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: learning what (not) to forget. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 144\u2013161. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01219-9_9"},{"key":"36_CR2","unstructured":"Bulatov, Y.: notMNIST dataset (2011). http:\/\/yaroslavvb.blogspot.com\/2011\/09\/notmnist-dataset.html"},{"key":"36_CR3","unstructured":"Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. In: NeurIPS (2020)"},{"key":"36_CR4","doi-asserted-by":"crossref","unstructured":"Cha, H., Lee, J., Shin, J.: Co$$^{2}$$L: contrastive continual learning. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00938"},{"key":"36_CR5","unstructured":"Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 2(7) (2020)"},{"key":"36_CR6","unstructured":"Chaudhry, A., Ranzato, M., Rohrbach, M., Elhoseiny, M.: Efficient lifelong learning with A-GEM. arXiv preprint arXiv:1812.00420 (2018)"},{"key":"36_CR7","unstructured":"Chaudhry, A., et al.: On tiny episodic memories in continual learning. arXiv preprint arXiv:1902.10486 (2019)"},{"key":"36_CR8","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: CVPR, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"36_CR9","unstructured":"Dosovitskiy, A., et al.: An image is worth $$16\\times 16$$ words: transformers for image recognition at scale. In: ICLR. OpenReview.net (2021). https:\/\/openreview.net\/forum?id=YicbFdNTTy"},{"key":"36_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1007\/978-3-030-58621-8_23","volume-title":"Computer Vision \u2013 ECCV 2020","author":"S Ebrahimi","year":"2020","unstructured":"Ebrahimi, S., Meier, F., Calandra, R., Darrell, T., Rohrbach, M.: Adversarial continual learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 386\u2013402. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58621-8_23"},{"key":"36_CR11","doi-asserted-by":"publisher","first-page":"1028","DOI":"10.1016\/j.tics.2020.09.004","volume":"24","author":"R Hadsell","year":"2020","unstructured":"Hadsell, R., Rao, D., Rusu, A.A., Pascanu, R.: Embracing change: continual learning in deep neural networks. Trends Cogni. Sci. 24, 1028\u20131040 (2020)","journal-title":"Trends Cogni. Sci."},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Hayes, T.L., Cahill, N.D., Kanan, C.: Memory efficient experience replay for streaming learning. In: ICRA (2019)","DOI":"10.1109\/ICRA.2019.8793982"},{"key":"36_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Hendrycks, D., et al.: The many faces of robustness: a critical analysis of out-of-distribution generalization. arXiv preprint arXiv:2006.16241 (2020)","DOI":"10.1109\/ICCV48922.2021.00823"},{"key":"36_CR15","unstructured":"Hu, E.J., et al.: LoRa: low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021)"},{"key":"36_CR16","unstructured":"Ke, Z., Liu, B., Huang, X.: Continual learning of a mixed sequence of similar and dissimilar tasks. In: NeurIPS 33 (2020)"},{"key":"36_CR17","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"13","key":"36_CR18","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1073\/pnas.1611835114","volume":"114","author":"J Kirkpatrick","year":"2017","unstructured":"Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. PNAS 114(13), 3521\u20133526 (2017)","journal-title":"PNAS"},{"key":"36_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1007\/978-3-030-58558-7_29","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Kolesnikov","year":"2020","unstructured":"Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., Houlsby, N.: Big Transfer (BiT): general visual representation learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12350, pp. 491\u2013507. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58558-7_29"},{"key":"36_CR20","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)"},{"issue":"7","key":"36_CR21","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1016\/j.tics.2016.05.004","volume":"20","author":"D Kumaran","year":"2016","unstructured":"Kumaran, D., Hassabis, D., McClelland, J.L.: What learning systems do intelligent agents need? Complementary learning systems theory updated. Trends Cogn. Sci. 20(7), 512\u2013534 (2016)","journal-title":"Trends Cogn. Sci."},{"key":"36_CR22","unstructured":"LeCun, Y.: The MNIST database of handwritten digits (1998). http:\/\/yann.lecun.com\/exdb\/mnist\/"},{"key":"36_CR23","doi-asserted-by":"crossref","unstructured":"Lester, B., Al-Rfou, R., Constant, N.: The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"36_CR24","doi-asserted-by":"crossref","unstructured":"Li, X.L., Liang, P.: Prefix-tuning: optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190 (2021)","DOI":"10.18653\/v1\/2021.acl-long.353"},{"key":"36_CR25","unstructured":"Li, X., Zhou, Y., Wu, T., Socher, R., Xiong, C.: Learn to grow: a continual structure learning framework for overcoming catastrophic forgetting. In: ICML, pp. 3925\u20133934. PMLR (2019)"},{"issue":"12","key":"36_CR26","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","volume":"40","author":"Z Li","year":"2017","unstructured":"Li, Z., Hoiem, D.: Learning without forgetting. TPAMI 40(12), 2935\u20132947 (2017)","journal-title":"TPAMI"},{"key":"36_CR27","doi-asserted-by":"crossref","unstructured":"Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G.: Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. arXiv preprint arXiv:2107.13586 (2021)","DOI":"10.1145\/3560815"},{"key":"36_CR28","doi-asserted-by":"crossref","unstructured":"Liu, X., Ji, K., Fu, Y., Du, Z., Yang, Z., Tang, J.: P-tuning v2: prompt tuning can be comparable to fine-tuning universally across scales and tasks. arXiv preprint arXiv:2110.07602 (2021)","DOI":"10.18653\/v1\/2022.acl-short.8"},{"key":"36_CR29","doi-asserted-by":"crossref","unstructured":"Lomonaco, V., Maltoni, D., Pellegrini, L.: Rehearsal-free continual learning over small non-IID batches. In: CVPR Workshops, pp. 989\u2013998 (2020)","DOI":"10.1109\/CVPRW50498.2020.00131"},{"key":"36_CR30","unstructured":"Loo, N., Swaroop, S., Turner, R.E.: Generalized variational continual learning. arXiv preprint arXiv:2011.12328 (2020)"},{"key":"36_CR31","unstructured":"Lopez-Paz, D., Ranzato, M.: Gradient episodic memory for continual learning. NeurIPS (2017)"},{"issue":"11","key":"36_CR32","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. JMLR 9(11), 2579\u20132605 (2008)","journal-title":"JMLR"},{"key":"36_CR33","doi-asserted-by":"crossref","unstructured":"Mai, Z., Li, R., Jeong, J., Quispe, D., Kim, H., Sanner, S.: Online continual learning in image classification: an empirical survey. arXiv preprint arXiv:2101.10423 (2021)","DOI":"10.1016\/j.neucom.2021.10.021"},{"key":"36_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1007\/978-3-030-01225-0_5","volume-title":"Computer Vision \u2013 ECCV 2018","author":"A Mallya","year":"2018","unstructured":"Mallya, A., Davis, D., Lazebnik, S.: Piggyback: adapting a single network to multiple tasks by learning to mask weights. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 72\u201388. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01225-0_5"},{"key":"36_CR35","doi-asserted-by":"crossref","unstructured":"Mallya, A., Lazebnik, S.: PackNet: adding multiple tasks to a single network by iterative pruning. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00810"},{"key":"36_CR36","unstructured":"Masana, M., Liu, X., Twardowski, B., Menta, M., Bagdanov, A.D., van de Weijer, J.: Class-incremental learning: survey and performance evaluation on image classification. arXiv preprint arXiv:2010.15277 (2020)"},{"issue":"3","key":"36_CR37","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1037\/0033-295X.102.3.419","volume":"102","author":"JL McClelland","year":"1995","unstructured":"McClelland, J.L., McNaughton, B.L., O\u2019Reilly, R.C.: Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychol. Rev. 102(3), 419 (1995)","journal-title":"Psychol. Rev."},{"key":"36_CR38","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/S0079-7421(08)60536-8","volume":"24","author":"M McCloskey","year":"1989","unstructured":"McCloskey, M., Cohen, N.J.: Catastrophic interference in connectionist networks: The sequential learning problem. Psychol. Learn. Motiv. 24, 109\u2013165 (1989)","journal-title":"Psychol. Learn. Motiv."},{"key":"36_CR39","unstructured":"Mehta, S.V., Patil, D., Chandar, S., Strubell, E.: An empirical investigation of the role of pre-training in lifelong learning. In: ICML Workshop (2021)"},{"key":"36_CR40","unstructured":"Mirzadeh, S.I., et al.: Architecture matters in continual learning. arXiv preprint arXiv:2202.00275 (2022)"},{"key":"36_CR41","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: NIPS (2011)"},{"key":"36_CR42","doi-asserted-by":"crossref","unstructured":"Pfeiffer, J., Kamath, A., R\u00fcckl\u00e9, A., Cho, K., Gurevych, I.: AdapterFusion: non-destructive task composition for transfer learning. arXiv preprint arXiv:2005.00247 (2020)","DOI":"10.18653\/v1\/2021.eacl-main.39"},{"key":"36_CR43","unstructured":"Pham, Q., Liu, C., Hoi, S.: DualNet: continual learning, fast and slow. In: NeurIPS 34 (2021)"},{"key":"36_CR44","unstructured":"Pham, Q., Liu, C., Sahoo, D., et al.: Contextual transformation networks for online continual learning. In: ICLR (2020)"},{"key":"36_CR45","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1007\/978-3-030-58536-5_31","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Prabhu","year":"2020","unstructured":"Prabhu, A., Torr, P.H.S., Dokania, P.K.: GDumb: a simple approach that questions our progress in continual learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 524\u2013540. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58536-5_31"},{"key":"36_CR46","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR 21, 1\u201367 (2020)","journal-title":"JMLR"},{"key":"36_CR47","unstructured":"Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C., Dosovitskiy, A.: Do vision transformers see like convolutional neural networks? In: NeurIPS 34 (2021)"},{"key":"36_CR48","unstructured":"Rajasegaran, J., Hayat, M., Khan, S.H., Khan, F.S., Shao, L.: Random path selection for continual learning. In: NeurIPS 32 (2019)"},{"key":"36_CR49","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: iCaRL: incremental classifier and representation learning. In: CVPR, pp. 2001\u20132010 (2017)","DOI":"10.1109\/CVPR.2017.587"},{"key":"36_CR50","unstructured":"Ridnik, T., Ben-Baruch, E., Noy, A., Zelnik-Manor, L.: ImageNet-21k pretraining for the masses. arXiv preprint arXiv:2104.10972 (2021)"},{"key":"36_CR51","unstructured":"Rusu, A.A., et al.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016)"},{"key":"36_CR52","unstructured":"Serra, J., Suris, D., Miron, M., Karatzoglou, A.: Overcoming catastrophic forgetting with hard attention to the task. In: ICML, pp. 4548\u20134557 (2018)"},{"key":"36_CR53","doi-asserted-by":"crossref","unstructured":"Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of SIGSAC Conference on Computer and Communications Security (2015)","DOI":"10.1145\/2810103.2813687"},{"key":"36_CR54","doi-asserted-by":"crossref","unstructured":"Smith, J., Balloch, J., Hsu, Y.C., Kira, Z.: Memory-efficient semi-supervised continual learning: the world is its own replay buffer. arXiv preprint arXiv:2101.09536 (2021)","DOI":"10.1109\/IJCNN52387.2021.9534361"},{"key":"36_CR55","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NeurIPS (2017)"},{"key":"36_CR56","doi-asserted-by":"crossref","unstructured":"Wang, R., et al.: K-adapter: Infusing knowledge into pre-trained models with adapters. arXiv preprint arXiv:2002.01808 (2020)","DOI":"10.18653\/v1\/2021.findings-acl.121"},{"key":"36_CR57","doi-asserted-by":"crossref","unstructured":"Wang, Z., Jian, T., Chowdhury, K., Wang, Y., Dy, J., Ioannidis, S.: Learn-prune-share for lifelong learning. In: ICDM (2020)","DOI":"10.1109\/ICDM50108.2020.00073"},{"key":"36_CR58","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: Learning to prompt for continual learning. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00024"},{"key":"36_CR59","unstructured":"Wortsman, M., et al.: Supermasks in superposition. arXiv preprint arXiv:2006.14769 (2020)"},{"key":"36_CR60","doi-asserted-by":"crossref","unstructured":"Wu, Y., et al.: Large scale incremental learning. In: CVPR, pp. 374\u2013382 (2019)","DOI":"10.1109\/CVPR.2019.00046"},{"key":"36_CR61","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":"36_CR62","doi-asserted-by":"crossref","unstructured":"Yan, S., Xie, J., He, X.: DER: dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014\u20133023 (2021)","DOI":"10.1109\/CVPR46437.2021.00303"},{"key":"36_CR63","unstructured":"Yoon, J., Yang, E., Lee, J., Hwang, S.J.: Lifelong learning with dynamically expandable networks. arXiv preprint arXiv:1708.01547 (2017)"},{"key":"36_CR64","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","volume-title":"Computer Vision \u2013 ECCV 2014","author":"MD Zeiler","year":"2014","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818\u2013833. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53"},{"key":"36_CR65","unstructured":"Zenke, F., Poole, B., Ganguli, S.: Continual learning through synaptic intelligence. In: ICML (2017)"},{"key":"36_CR66","unstructured":"Zeno, C., Golan, I., Hoffer, E., Soudry, D.: Task agnostic continual learning using online variational bayes. arXiv preprint arXiv:1803.10123 (2018)"},{"key":"36_CR67","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.neunet.2022.02.001","volume":"149","author":"T Zhao","year":"2022","unstructured":"Zhao, T., Wang, Z., Masoomi, A., Dy, J.: Deep Bayesian unsupervised lifelong learning. Neural Netw. 149, 95\u2013106 (2022)","journal-title":"Neural Netw."}],"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\/978-3-031-19809-0_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T00:22:22Z","timestamp":1667434942000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19809-0_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198083","9783031198090"],"references-count":67,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19809-0_36","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":"1 November 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)"}}]}}