{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T07:36:28Z","timestamp":1743147388077,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030594091"},{"type":"electronic","value":"9783030594107"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-59410-7_11","type":"book-chapter","created":{"date-parts":[[2020,9,21]],"date-time":"2020-09-21T16:57:43Z","timestamp":1600707463000},"page":"178-194","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Completely Unsupervised Cross-Modal Hashing"],"prefix":"10.1007","author":[{"given":"Jiasheng","family":"Duan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengfei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zi","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,18]]},"reference":[{"key":"11_CR1","unstructured":"Andrew, G., Arora, R., Bilmes, J.A., Livescu, K.: Deep canonical correlation analysis. In: ICML (2013)"},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Bronstein, M.M., Bronstein, A.M., Michel, F., Paragios, N.: Data fusion through cross-modality metric learning using similarity-sensitive hashing. In: CVPR (2010)","DOI":"10.1109\/CVPR.2010.5539928"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Chua, T., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y.: NUS-WIDE: a real-world web image database from national university of Singapore. In: CIVR (2009)","DOI":"10.1145\/1646396.1646452"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Ding, G., Guo, Y., Zhou, J.: Collective matrix factorization hashing for multimodal data. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.267"},{"key":"11_CR5","unstructured":"Frome, A., et al.: Devise: a deep visual-semantic embedding model. In: NeurIPS (2013)"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Fu, Z., Tan, X., Peng, N., Zhao, D., Yan, R.: Style transfer in text: exploration and evaluation. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11330"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.265"},{"key":"11_CR8","unstructured":"Goodfellow, I.J., et al.: Generative adversarial nets. In: NeurIPS (2014)"},{"issue":"12","key":"11_CR9","doi-asserted-by":"publisher","first-page":"2639","DOI":"10.1162\/0899766042321814","volume":"16","author":"DR Hardoon","year":"2004","unstructured":"Hardoon, D.R., Szedm\u00e1k, S., Shawe-Taylor, J.: Canonical correlation analysis: an overview with application to learning methods. Neural Comput. 16(12), 2639\u20132664 (2004)","journal-title":"Neural Comput."},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Hu, Y., Jin, Z., Ren, H., Cai, D., He, X.: Iterative multi-view hashing for cross media indexing. In: ACMMM (2014)","DOI":"10.1145\/2647868.2654906"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Huiskes, M.J., Lew, M.S.: The MIR flickr retrieval evaluation. In: SIGMM (2008)","DOI":"10.1145\/1460096.1460104"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Jiang, Q.Y., Li, W.J.: Deep cross-modal hashing. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.348"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. In: EMNLP (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"11_CR14","unstructured":"Kumar, S., Udupa, R.: Learning hash functions for cross-view similarity search. In: IJCAI (2011)"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Li, C., Deng, C., Wang, L., Xie, D., Liu, X.: Coupled cyclegan: unsupervised hashing network for cross-modal retrieval. In: AAAI (2019)","DOI":"10.1609\/aaai.v33i01.3301176"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Li, D., Dimitrova, N., Li, M., Sethi, I.K.: Multimedia content processing through cross-modal association. In: ACMMM (2003)","DOI":"10.1145\/957013.957143"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Lin, Z., Ding, G., Hu, M., Wang, J.: Semantics-preserving hashing for cross-view retrieval. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7299011"},{"key":"11_CR18","unstructured":"Liu, W., Mu, C., Kumar, S., Chang, S.: Discrete graph hashing. In: NeurIPS (2014)"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Long, M., Cao, Y., Wang, J., Yu, P.S.: Composite correlation quantization for efficient multimodal retrieval. In: SIGIR (2016)","DOI":"10.1145\/2911451.2911493"},{"key":"11_CR20","unstructured":"Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: ICML (2011)"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Rasiwasia, N., et al.: A new approach to cross-modal multimedia retrieval. In: ACMMM (2010)","DOI":"10.1145\/1873951.1873987"},{"key":"11_CR22","unstructured":"Rastegari, M., Choi, J., Fakhraei, S., Hal III, H., Davis, L.S.: Predictable dual-view hashing. In: ICML (2013)"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Rosipal, R., Kr\u00e4mer, N.: Overview and recent advances in partial least squares. In: SLSFS (2005)","DOI":"10.1007\/11752790_2"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Sharma, A., Kumar, A., Daum\u00e9, H., Jacobs, D.W.: Generalized multiview analysis: a discriminative latent space. In: CVPR (2012)","DOI":"10.1109\/CVPR.2012.6247923"},{"key":"11_CR25","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Song, J., Yang, Y., Yang, Y., Huang, Z., Shen, H.T.: Inter-media hashing for large-scale retrieval from heterogeneous data sources. In: SIGMOD (2013)","DOI":"10.1145\/2463676.2465274"},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Sun, L., Ji, S., Ye, J.: A least squares formulation for canonical correlation analysis. In: ICML (2008)","DOI":"10.1145\/1390156.1390285"},{"key":"11_CR28","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.1162\/089976600300015349","volume":"12","author":"JB Tenenbaum","year":"2000","unstructured":"Tenenbaum, J.B., Freeman, W.T.: Separating style and content with bilinear models. Neural Comput. 12, 1247\u20131283 (2000)","journal-title":"Neural Comput."},{"key":"11_CR29","unstructured":"Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: NeurIPS (2008)"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Wu, G., et al.: Unsupervised deep hashing via binary latent factor models for large-scale cross-modal retrieval. In: IJCAI (2018)","DOI":"10.24963\/ijcai.2018\/396"},{"issue":"4","key":"11_CR31","doi-asserted-by":"publisher","first-page":"1602","DOI":"10.1109\/TIP.2018.2878970","volume":"28","author":"L Wu","year":"2019","unstructured":"Wu, L., Wang, Y., Shao, L.: Cycle-consistent deep generative hashing for cross-modal retrieval. IEEE Trans. Image Process. 28(4), 1602\u20131612 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"11_CR32","doi-asserted-by":"publisher","first-page":"2494","DOI":"10.1109\/TIP.2017.2676345","volume":"26","author":"X Xu","year":"2017","unstructured":"Xu, X., Shen, F., Yang, Y., Shen, H.T., Li, X.: Learning discriminative binary codes for large-scale cross-modal retrieval. IEEE Trans. Image Process. 26, 2494\u20132507 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"11_CR33","doi-asserted-by":"crossref","unstructured":"Ye, Z., Peng, Y.: Multi-scale correlation for sequential cross-modal hashing learning. In: ACMMM (2018)","DOI":"10.1145\/3240508.3240560"},{"key":"11_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, D., Li, W.J.: Large-scale supervised multimodal hashing with semantic correlation maximization. In: AAAI (2014)","DOI":"10.1609\/aaai.v28i1.8995"},{"key":"11_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, J., Peng, Y., Yuan, M.: Unsupervised generative adversarial cross-modal hashing. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11263"},{"key":"11_CR36","unstructured":"Zhen, Y., Yeung, D.: Co-regularized hashing for multimodal data. In: NeurIPS (2012)"},{"key":"11_CR37","doi-asserted-by":"crossref","unstructured":"Zhou, J., Ding, G., Guo, Y.: Latent semantic sparse hashing for cross-modal similarity search. In: SIGIR (2014)","DOI":"10.1145\/2600428.2609610"},{"key":"11_CR38","doi-asserted-by":"crossref","unstructured":"Zhu, J., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59410-7_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:49:02Z","timestamp":1710269342000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59410-7_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030594091","9783030594107"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59410-7_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"18 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jeju","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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":"24 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/db.pknu.ac.kr\/dasfaa2020\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"487","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":"119","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":"23","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":"24% - 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":"3.11","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":"6.81","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":"15 demo papers and 4 industrial papers","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)"}}]}}