{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:38:50Z","timestamp":1777657130980,"version":"3.51.4"},"publisher-location":"Cham","reference-count":62,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200588","type":"print"},{"value":"9783031200595","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\/978-3-031-20059-5_40","type":"book-chapter","created":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T16:02:50Z","timestamp":1666972970000},"page":"700-716","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for\u00a0Image-Text Retrieval"],"prefix":"10.1007","author":[{"given":"Haoran","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongliang","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boyang","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunlong","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhong","family":"Ji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Errui","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingdong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,29]]},"reference":[{"key":"40_CR1","doi-asserted-by":"crossref","unstructured":"Anderson, P., et al.: Bottom-up and top-down attention for image captioning and vqa. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00636"},{"key":"40_CR2","doi-asserted-by":"crossref","unstructured":"Antol, S., et al.: Vqa: visual question answering. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.279"},{"key":"40_CR3","doi-asserted-by":"crossref","unstructured":"Bai, Y., Fu, J., Zhao, T., Mei, T.: Deep attention neural tensor network for visual question answering. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01258-8_2"},{"key":"40_CR4","doi-asserted-by":"crossref","unstructured":"Chen, H., Ding, G., Liu, X., Lin, Z., Liu, J., Han, J.: Imram: iterative matching with recurrent attention memory for cross-modal image-text retrieval. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01267"},{"key":"40_CR5","doi-asserted-by":"crossref","unstructured":"Chen, J., Hu, H., Wu, H., Jiang, Y., Wang, C.: Learning the best pooling strategy for visual semantic embedding. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01553"},{"key":"40_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1007\/978-3-030-58601-0_33","volume-title":"Computer Vision \u2013 ECCV 2020","author":"T Chen","year":"2020","unstructured":"Chen, T., Deng, J., Luo, J.: Adaptive offline quintuplet loss for image-text matching. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12358, pp. 549\u2013565. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58601-0_33"},{"key":"40_CR7","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: ICML (2020)"},{"key":"40_CR8","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: NAACL (2019)"},{"key":"40_CR9","doi-asserted-by":"crossref","unstructured":"Diao, H., Zhang, Y., Ma, L., Lu, H.: Similarity reasoning and filtration for image-text matching. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i2.16209"},{"key":"40_CR10","unstructured":"Faghri, F., Fleet, D.J., Kiros, J., Fidler, S.: Vse++: improved visual-semantic embeddings. In: BMVC (2018)"},{"key":"40_CR11","unstructured":"Frome, A., et al.: Devise: a deep visual-semantic embedding model. In: NeurIPS (2013)"},{"key":"40_CR12","doi-asserted-by":"crossref","unstructured":"Ge, X., Chen, F., Jose, J.M., Ji, Z., Wu, Z., Liu, X.: Structured multi-modal feature embedding and alignment for image-sentence retrieval. In: ACMMM (2021)","DOI":"10.1145\/3474085.3475634"},{"key":"40_CR13","unstructured":"Grill, J.B., et al.: Bootstrap your own latent-a new approach to self-supervised learning. In: NeurIPS (2020)"},{"key":"40_CR14","unstructured":"Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: CVPR (2006)"},{"key":"40_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: CVPR, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"40_CR16","doi-asserted-by":"crossref","unstructured":"Hua, T., Zheng, H., Bai, Y., Zhang, W., Zhang, X.P., Mei, T.: Exploiting relationship for complex-scene image generation. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i2.16250"},{"key":"40_CR17","doi-asserted-by":"crossref","unstructured":"Huang, Y., Wu, Q., Song, C., Wang, L.: Learning semantic concepts and order for image and sentence matching. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00645"},{"key":"40_CR18","unstructured":"Huo, Y., et al.: Wenlan: bridging vision and language by large-scale multi-modal pre-training. arXiv preprint arXiv:2103.06561 (2021)"},{"issue":"8","key":"40_CR19","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","volume":"31","author":"A Jain","year":"2010","unstructured":"Jain, A.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31(8), 651\u2013666 (2010)","journal-title":"Pattern Recogn. Lett."},{"key":"40_CR20","doi-asserted-by":"crossref","unstructured":"Ji, Z., Chen, K., Wang, H.: Step-wise hierarchical alignment network for image-text matching. In: IJCAI (2021)","DOI":"10.24963\/ijcai.2021\/106"},{"key":"40_CR21","doi-asserted-by":"crossref","unstructured":"Jiao, Y., Jie, Z., Chen, J., Ma, L., Jiang, Y.G.: Suspected object matters: rethinking model\u2019s prediction for one-stage visual grounding. ArXiv:2203.05186 (2022)","DOI":"10.1145\/3581783.3611721"},{"key":"40_CR22","doi-asserted-by":"crossref","unstructured":"Jiao, Y., et al.: Two-stage visual cues enhancement network for referring image segmentation. In: ACM MM (2021)","DOI":"10.1145\/3474085.3475222"},{"key":"40_CR23","doi-asserted-by":"crossref","unstructured":"Karpathy, A., Li, F.F.: Deep visual-semantic alignments for generating image descriptions. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298932"},{"key":"40_CR24","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2014)"},{"key":"40_CR25","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2016)"},{"key":"40_CR26","unstructured":"Kiros, R., Salakhutdinov, R., Zemel, R.: Unifying visual-semantic embeddings with multimodal neural language models. In: NeurIPS Workshop (2014)"},{"issue":"7553","key":"40_CR27","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"40_CR28","doi-asserted-by":"crossref","unstructured":"Lee, K.H., Chen, X., Hua, G., Hu, H., He, X.: Stacked cross attention for image-text matching. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01225-0_13"},{"key":"40_CR29","doi-asserted-by":"crossref","unstructured":"Li, K., Zhang, Y., Li, K., Li, Y., Fu, Y.: Visual semantic reasoning for image-text matching. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00475"},{"key":"40_CR30","doi-asserted-by":"crossref","unstructured":"Li, W., et al.: Unimo: towards unified-modal understanding and generation via cross-modal contrastive learning. In: ACL (2021)","DOI":"10.18653\/v1\/2021.acl-long.202"},{"key":"40_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"40_CR32","doi-asserted-by":"crossref","unstructured":"Liu, C., Mao, Z., Zhang, T., Xie, H., Wang, B., Zhang, Y.: Graph structured network for image-text matching. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01093"},{"key":"40_CR33","doi-asserted-by":"crossref","unstructured":"Liu, F., Ye, R., Wang, X., Li, S.: Hal: improved text-image matching by mitigating visual semantic hubs. In: AAAI (2020)","DOI":"10.1609\/aaai.v34i07.6823"},{"key":"40_CR34","doi-asserted-by":"crossref","unstructured":"Liu, S., Fan, H., Qian, S., Chen, Y., Ding, W., Wang, Z.: Hit: hierarchical transformer with momentum contrast for video-text retrieval. arXiv preprint arXiv:2103.15049 (2021)","DOI":"10.1109\/ICCV48922.2021.01170"},{"key":"40_CR35","doi-asserted-by":"crossref","unstructured":"Luo, H., et al.: Clip4clip: an empirical study of clip for end to end video clip retrieval. ArXiv arXiv:abs\/2104.08860 (2021)","DOI":"10.1016\/j.neucom.2022.07.028"},{"key":"40_CR36","doi-asserted-by":"crossref","unstructured":"Ma, L., Lu, Z., Shang, L., Li, H.: Multimodal convolutional neural networks for matching image and sentence. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.301"},{"key":"40_CR37","doi-asserted-by":"crossref","unstructured":"Ma, L., Lu, Z., Li, H.: Learning to answer questions from image using convolutional neural network. In: AAAI (2016)","DOI":"10.1609\/aaai.v30i1.10442"},{"key":"40_CR38","unstructured":"Mao, J., Xu, W., Yang, Y., Wang, J., Yuille, A.: Deep captioning with multimodal recurrent neural networks (m-rnn). In: ICLR (2015)"},{"key":"40_CR39","unstructured":"Oord, A.V.D., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)"},{"key":"40_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":"40_CR41","doi-asserted-by":"crossref","unstructured":"Plummer, B.A., Wang, L., Cervantes, C., Caicedo, J.C., Hockenmaier, J., Lazebnik, S.: Flickr30k entities: collecting region-to-phrase correspondences for richer image-to-sentence models. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.303"},{"key":"40_CR42","doi-asserted-by":"crossref","unstructured":"Qu, L., Liu, M., Wu, J., Gao, Z., Nie, L.: Dynamic modality interaction modeling for image-text retrieval. In: SIGIR (2021)","DOI":"10.1145\/3404835.3462829"},{"key":"40_CR43","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. arXiv preprint arXiv:2103.00020 (2021)"},{"key":"40_CR44","unstructured":"Ren, S., He, K., Girshick, R.B., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: NeurIPS (2015)"},{"key":"40_CR45","doi-asserted-by":"crossref","unstructured":"Shi, B., Ji, L., Lu, P., Niu, Z., Duan, N.: Knowledge aware semantic concept expansion for image-text matching. In: IJCAI (2019)","DOI":"10.24963\/ijcai.2019\/720"},{"key":"40_CR46","doi-asserted-by":"crossref","unstructured":"Song, Y., Soleymani, M.: Polysemous visual-semantic embedding for cross-modal retrieval (2019)","DOI":"10.1109\/CVPR.2019.00208"},{"key":"40_CR47","unstructured":"Tanmay, G., Alexander, G.S., Derek, H.: Vico: word embeddings from visual co-occurrences. In: ICCV (2019)"},{"key":"40_CR48","doi-asserted-by":"crossref","unstructured":"Wang, B., Ma, L., Zhang, W., Liu, W.: Reconstruction network for video captioning. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00795"},{"key":"40_CR49","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/978-3-030-58586-0_2","volume-title":"Computer Vision \u2013 ECCV 2020","author":"H Wang","year":"2020","unstructured":"Wang, H., Zhang, Y., Ji, Z., Pang, Y., Ma, L.: Consensus-aware visual-semantic embedding for image-text matching. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12369, pp. 18\u201334. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58586-0_2"},{"key":"40_CR50","doi-asserted-by":"crossref","unstructured":"Wang, L., Li, Y., Lazebnik, S.: Learning deep structure-preserving image-text embeddings. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.541"},{"key":"40_CR51","doi-asserted-by":"crossref","unstructured":"Wang, S., Wang, R., Yao, Z., Shan, S., Chen, X.: Cross-modal scene graph matching for relationship-aware image-text retrieval. In: WACV (2020)","DOI":"10.1109\/WACV45572.2020.9093614"},{"key":"40_CR52","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Wasserstein coupled graph learning for cross-modal retrieval. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00183"},{"key":"40_CR53","doi-asserted-by":"crossref","unstructured":"Wehrmann, J., Kolling, C., Barros, R.C.: Adaptive cross-modal embeddings for image-text alignment. In: AAAI (2020)","DOI":"10.1609\/aaai.v34i07.6915"},{"key":"40_CR54","doi-asserted-by":"crossref","unstructured":"Wei, J., Xu, X., Yang, Y., Ji, Y., Wang, Z., Shen, H.T.: Universal weighting metric learning for cross-modal matching. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01302"},{"key":"40_CR55","doi-asserted-by":"crossref","unstructured":"Wei, X., Zhang, T., Li, Y., Zhang, Y., Wu, F.: Multi-modality cross attention network for image and sentence matching. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01095"},{"key":"40_CR56","doi-asserted-by":"publisher","first-page":"2866","DOI":"10.1109\/TCSVT.2020.3030656","volume":"31","author":"K Wen","year":"2021","unstructured":"Wen, K., Gu, X., Cheng, Q.: Learning dual semantic relations with graph attention for image-text matching. IEEE Trans. Circ. Syst. Video Technol. 31, 2866\u20132879 (2021)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"40_CR57","unstructured":"Wu, W., Sun, Z., Ouyang, W.: Transferring textual knowledge for visual recognition. ArXiv:2207.01297 (2022)"},{"key":"40_CR58","doi-asserted-by":"crossref","unstructured":"Xu, D., Zhu, Y., Choy, C.B., Fei-Fei, L.: Scene graph generation by iterative message passing. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.330"},{"key":"40_CR59","unstructured":"Xu, K., et al.: Show, attend and tell: neural image caption generation with visual attention. In: ICML (2015)"},{"key":"40_CR60","doi-asserted-by":"crossref","unstructured":"Yang, J., et al.: Unified contrastive learning in image-text-label space. ArXiv arXiv:2204.03610 (2022)","DOI":"10.1109\/CVPR52688.2022.01857"},{"key":"40_CR61","unstructured":"Zhang, L., et al.: Vldeformer: learning visual-semantic embeddings by vision-language transformer decomposing. ArXiv arXiv:2110.11338 (2021)"},{"key":"40_CR62","doi-asserted-by":"crossref","unstructured":"Zhao, D., Wang, A., Russakovsky, O.: Understanding and evaluating racial biases in image captioning (2021)","DOI":"10.1109\/ICCV48922.2021.01456"}],"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\/978-3-031-20059-5_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T18:59:49Z","timestamp":1701284389000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20059-5_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200588","9783031200595"],"references-count":62,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20059-5_40","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":"29 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)"}}]}}