{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:25:20Z","timestamp":1782779120277,"version":"3.54.5"},"publisher-location":"Cham","reference-count":61,"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_10","type":"book-chapter","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T09:04:58Z","timestamp":1666343098000},"page":"163-181","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Adaptive Fine-Grained Sketch-Based Image Retrieval"],"prefix":"10.1007","author":[{"given":"Ayan Kumar","family":"Bhunia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aneeshan","family":"Sain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Parth Hiren","family":"Shah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Animesh","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pinaki Nath","family":"Chowdhury","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Zhe","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,22]]},"reference":[{"key":"10_CR1","unstructured":"Antoniou, A., Edwards, H., Storkey, A.: How to train your MAML. In: ICLR (2018)"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Bhunia, A.K., Chowdhury, P.N., Sain, A., Yang, Y., Xiang, T., Song, Y.Z.: More photos are all you need: Semi-supervised learning for fine-grained sketch based image retrieval. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00423"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Bhunia, A.K., Chowdhury, P.N., Yang, Y., Hospedales, T.M., Xiang, T., Song, Y.Z.: Vectorization and rasterization: self-supervised learning for sketch and handwriting. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00562"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Bhunia, A.K., et al.: Doodle it yourself: class incremental learning by drawing a few sketches. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00233"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Bhunia, A.K., Ghose, S., Kumar, A., Chowdhury, P.N., Sain, A., Song, Y.Z.: MetaHTR: towards writer-adaptive handwritten text recognition. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01557"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Bhunia, A.K., et al: Sketching without worrying: noise-tolerant sketch-based image retrieval. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00107"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Bhunia, A.K., Yang, Y., Hospedales, T.M., Xiang, T., Song, Y.Z.: Sketch less for more: On-the-fly fine-grained sketch based image retrieval. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00980"},{"key":"10_CR8","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1162\/tacl_a_00051","volume":"5","author":"P Bojanowski","year":"2017","unstructured":"Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. TACL 5, 135\u2013146 (2017)","journal-title":"TACL"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Choi, M., Choi, J., Baik, S., Kim, T.H., Lee, K.M.: Scene-adaptive video frame interpolation via meta-learning. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00946"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Chowdhury, P.N., Bhunia, A.K., Gajjala, V.R., Sain, A., Xiang, T., Song, Y.Z.: Partially does it: towards scene-level FG-SBIR with partial input. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00243"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Chowdhury, P.N., Sain, A., Bhunia, A.K., Xiang, T., Gryaditskaya, Y., Song, Y.Z.: FS-COCO: towards understanding of freehand sketches of common objects in context. In: ECCV (2022)","DOI":"10.1007\/978-3-031-20074-8_15"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Collomosse, J., Bui, T., Jin, H.: LiveSketch: query perturbations for guided sketch-based visual search. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00299"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Collomosse, J., Bui, T., Wilber, M.J., Fang, C., Jin, H.: Sketching with style: Visual search with sketches and aesthetic context. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.290"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Dey, S., Riba, P., Dutta, A., Llados, J., Song, Y.Z.: Doodle to search: practical zero-shot sketch-based image retrieval. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00228"},{"key":"10_CR15","unstructured":"Dou, Q., de Castro, D.C., Kamnitsas, K., Glocker, B.: Domain generalization via model-agnostic learning of semantic features. In: NeurIPS (2019)"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Dutta, A., Akata, Z.: Semantically tied paired cycle consistency for zero-shot sketch-based image retrieval. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00523"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Dutta, A., Akata, Z.: Semantically tied paired cycle consistency for any-shot sketch-based image retrieval. IJCV (2020)","DOI":"10.1109\/CVPR.2019.00523"},{"key":"10_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1007\/978-3-030-58558-7_21","volume-title":"Computer Vision \u2013 ECCV 2020","author":"T Dutta","year":"2020","unstructured":"Dutta, T., Singh, A., Biswas, S.: Adaptive margin diversity regularizer for handling data imbalance in zero-Shot SBIR. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12350, pp. 349\u2013364. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58558-7_21"},{"key":"10_CR19","unstructured":"Fan, C., Ram, P., Liu, S.: Sign-MAML: efficient model-agnostic meta-learning by SignSGD. arXiv preprint arXiv:2109.07497 (2021)"},{"key":"10_CR20","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: ICML (2017)"},{"key":"10_CR21","unstructured":"Finn, C., Xu, K., Levine, S.: Probabilistic model-agnostic meta-learning. In: NeurIPS (2018)"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Fu, Z., Xiang, T., Kodirov, E., Gong, S.: Zero-shot object recognition by semantic manifold distance. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298879"},{"key":"10_CR23","unstructured":"Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: ICML (2015)"},{"key":"10_CR24","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1007\/s11257-019-09248-1","volume":"30","author":"E Garcia-Ceja","year":"2020","unstructured":"Garcia-Ceja, E., Riegler, M., Kvernberg, A.K., Torresen, J.: User-adaptive models for activity and emotion recognition using deep transfer learning and data augmentation. User Model. User-Adapt. Interact. 30, 365\u2013393 (2020). https:\/\/doi.org\/10.1007\/s11257-019-09248-1","journal-title":"User Model. User-Adapt. Interact."},{"key":"10_CR25","first-page":"1279","volume":"42","author":"S Horiguchi","year":"2019","unstructured":"Horiguchi, S., Ikami, D., Aizawa, K.: Significance of Softmax-based features in comparison to distance metric learning-based features. IEEE-TPAMI 42, 1279\u20131285 (2019)","journal-title":"IEEE-TPAMI"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Hospedales, T., Antoniou, A., Micaelli, P., Storkey, A.: Meta-learning in neural networks: a survey. arXiv preprint arXiv:2004.05439 (2020)","DOI":"10.1109\/TPAMI.2021.3079209"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Hsieh, P.L., Ma, C., Yu, J., Li, H.: Unconstrained realtime facial performance capture. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298776"},{"key":"10_CR28","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Lane, N.D., et al.: Enabling large-scale human activity inference on smartphones using community similarity networks (CSN). In: UbiComp (2011)","DOI":"10.1145\/2030112.2030160"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Li, Y., Hospedales, T.M., Song, Y.Z., Gong, S.: Fine-grained sketch-based image retrieval by matching deformable part models. In: BMVC (2014)","DOI":"10.5244\/C.28.115"},{"key":"10_CR31","unstructured":"Li, Z., Zhou, F., Chen, F., Li, H.: Meta-SGD: learning to learn quickly for few-shot learning. arXiv preprint arXiv:1707.09835 (2017)"},{"key":"10_CR32","doi-asserted-by":"crossref","unstructured":"Liu, L., Shen, F., Shen, Y., Liu, X., Shao, L.: Deep sketch hashing: fast free-hand sketch-based image retrieval. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.247"},{"key":"10_CR33","doi-asserted-by":"crossref","unstructured":"Liu, Q., Xie, L., Wang, H., Yuille, A.: Semantic-aware knowledge preservation for zero-shot sketch-based image retrieval. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00376"},{"key":"10_CR34","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: ICLR (2014)"},{"key":"10_CR35","unstructured":"Oreshkin, B., L\u00f3pez, P.R., Lacoste, A.: TADAM: task dependent adaptive metric for improved few-shot learning. In: NeurIPS (2018)"},{"key":"10_CR36","doi-asserted-by":"crossref","unstructured":"Pang, K., et al.: Generalising fine-grained sketch-based image retrieval. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00077"},{"key":"10_CR37","doi-asserted-by":"crossref","unstructured":"Pang, K., Song, Y.Z., Xiang, T., Hospedales, T.M.: Cross-domain generative learning for fine-grained sketch-based image retrieval. In: BMVC (2017)","DOI":"10.5244\/C.31.46"},{"key":"10_CR38","doi-asserted-by":"crossref","unstructured":"Pang, K., Yang, Y., Hospedales, T.M., Xiang, T., Song, Y.Z.: Solving mixed-modal jigsaw puzzle for fine-grained sketch-based image retrieval. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01036"},{"key":"10_CR39","unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch. In: NeurIPS Autodiff Workshop (2017)"},{"key":"10_CR40","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: EMNLP (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"10_CR41","unstructured":"Raghu, A., Raghu, M., Bengio, S., Vinyals, O.: Rapid learning or feature reuse? Towards understanding the effectiveness of MAML. In: ICLR (2020)"},{"key":"10_CR42","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. IJCV 115, 211\u2013252 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"IJCV"},{"key":"10_CR43","unstructured":"Rusu, A.A., et al.: Meta-learning with latent embedding optimization. In: ICLR (2019)"},{"key":"10_CR44","doi-asserted-by":"crossref","unstructured":"Sain, A., Bhunia, A.K., Potlapalli, V., Chowdhury, P.N., Xiang, T., Song, Y.Z.: Sketch3T: test-time training for zero-shot SBIR. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00731"},{"key":"10_CR45","unstructured":"Sain, A., Bhunia, A.K., Yang, Y., Xiang, T., Song, Y.Z.: Cross-modal hierarchical modelling forfine-grained sketch based image retrieval. In: BMVC (2020)"},{"key":"10_CR46","doi-asserted-by":"crossref","unstructured":"Sain, A., Bhunia, A.K., Yang, Y., Xiang, T., Song, Y.Z.: StyleMeUp: towards style-agnostic sketch-based image retrieval. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00840"},{"key":"10_CR47","doi-asserted-by":"crossref","unstructured":"Sampaio Ferraz Ribeiro, L., Bui, T., Collomosse, J., Ponti, M.: Sketchformer: transformer-based representation for sketched structure. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01416"},{"key":"10_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2897824.2925954","volume":"35","author":"P Sangkloy","year":"2016","unstructured":"Sangkloy, P., Burnell, N., Ham, C., Hays, J.: The sketchy database: learning to retrieve badly drawn bunnies. ACM TOG 35, 1\u201312 (2016)","journal-title":"ACM TOG"},{"key":"10_CR49","doi-asserted-by":"crossref","unstructured":"Shen, Y., Liu, L., Shen, F., Shao, L.: Zero-shot sketch-image hashing. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00379"},{"key":"10_CR50","unstructured":"Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few shot learning. In: NeurIPS (2017)"},{"key":"10_CR51","doi-asserted-by":"crossref","unstructured":"Soh, H., Sanner, S., White, M., Jamieson, G.: Deep sequential recommendation for personalized adaptive user interfaces. In: IUI (2017)","DOI":"10.1145\/3025171.3025207"},{"key":"10_CR52","doi-asserted-by":"crossref","unstructured":"Song, J., Song, Y.Z., Xiang, T., Hospedales, T.M.: Fine-grained image retrieval: the text\/sketch input dilemma. In: BMVC (2017)","DOI":"10.5244\/C.31.45"},{"key":"10_CR53","doi-asserted-by":"crossref","unstructured":"Song, J., Yu, Q., Song, Y.Z., Xiang, T., Hospedales, T.M.: Deep spatial-semantic attention for fine-grained sketch-based image retrieval. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.592"},{"key":"10_CR54","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1007\/978-3-030-58568-6_16","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Tian","year":"2020","unstructured":"Tian, Y., Wang, Y., Krishnan, D., Tenenbaum, J.B., Isola, P.: Rethinking few-shot image classification: a good embedding is all you need? In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 266\u2013282. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58568-6_16"},{"key":"10_CR55","unstructured":"Wang, F., Kang, L., Li, Y.: Sketch-based 3d shape retrieval using convolutional neural networks. In: CVPR (2015)"},{"key":"10_CR56","first-page":"207","volume":"10","author":"KQ Weinberger","year":"2009","unstructured":"Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. JMLR 10, 207\u2013244 (2009)","journal-title":"JMLR"},{"key":"10_CR57","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1007\/978-3-030-01225-0_19","volume-title":"Computer Vision \u2013 ECCV 2018","author":"SK Yelamarthi","year":"2018","unstructured":"Yelamarthi, S.K., Reddy, S.K., Mishra, A., Mittal, A.: A zero-shot framework for sketch based image retrieval. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 316\u2013333. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01225-0_19"},{"key":"10_CR58","doi-asserted-by":"crossref","unstructured":"Yu, Q., Liu, F., Song, Y.Z., Xiang, T., Hospedales, T.M., Loy, C.C.: Sketch me that shoe. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.93"},{"key":"10_CR59","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1007\/s11263-020-01382-3","volume":"129","author":"Q Yu","year":"2021","unstructured":"Yu, Q., Song, J., Song, Y.Z., Xiang, T., Hospedales, T.M.: Fine-grained instance-level sketch-based image retrieval. IJCV 129, 484\u2013500 (2021). https:\/\/doi.org\/10.1007\/s11263-020-01382-3","journal-title":"IJCV"},{"key":"10_CR60","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1007\/s11263-016-0932-3","volume":"122","author":"Q Yu","year":"2017","unstructured":"Yu, Q., Yang, Y., Liu, F., Song, Y.Z., Xiang, T., Hospedales, T.M.: Sketch-a-Net: a deep neural network that beats humans. IJCV 122, 411\u2013425 (2017). https:\/\/doi.org\/10.1007\/s11263-016-0932-3","journal-title":"IJCV"},{"key":"10_CR61","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1007\/978-3-030-01216-8_19","volume-title":"Computer Vision \u2013 ECCV 2018","author":"J Zhang","year":"2018","unstructured":"Zhang, J., et al.: Generative domain-migration hashing for sketch-to-image retrieval. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 304\u2013321. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01216-8_19"}],"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_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T11:31:48Z","timestamp":1678361508000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19836-6_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198359","9783031198366"],"references-count":61,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19836-6_10","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)"}}]}}