{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:02:50Z","timestamp":1742958170402,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031202322"},{"type":"electronic","value":"9783031202339"}],"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\/978-3-031-20233-9_51","type":"book-chapter","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T00:02:48Z","timestamp":1667433768000},"page":"501-510","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MDF-Net: Multimodal Deep Fusion for\u00a0Large-Scale Product Recognition"],"prefix":"10.1007","author":[{"given":"Yanling","family":"Pan","sequence":"first","affiliation":[]},{"given":"Ruizhi","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Weijuan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Delong","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Fan","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"key":"51_CR1","doi-asserted-by":"crossref","unstructured":"Zhong, C., Jiang, L., Liang, Y., Sun, H., Ma, C.: Temporal multiple-convolutional network for commodity classification of online retail platform data. In: Proceedings of the 2020 12th International Conference on Machine Learning and Computing, pp. 236\u2013241 (2020)","DOI":"10.1145\/3383972.3383989"},{"key":"51_CR2","unstructured":"Zahera, H.M., Sherif, M.: ProBERT: product data classification with fine-tuning BERT model. In: MWPD@ ISWC (2020)"},{"issue":"9","key":"51_CR3","doi-asserted-by":"publisher","first-page":"1939","DOI":"10.3390\/app9091939","volume":"9","author":"Y Yang","year":"2019","unstructured":"Yang, Y., Wang, X., Zhao, Q., Sui, T.: Two-level attentions and grouping attention convolutional network for fine-grained image classification. Appl. Sci. 9(9), 1939 (2019)","journal-title":"Appl. Sci."},{"key":"51_CR4","doi-asserted-by":"crossref","unstructured":"Morency, L.P., Liang, P.P., Zadeh, A.: Tutorial on multimodal machine learning. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts, Seattle, United States, pp. 33\u201338. Association for Computational Linguistics, July 2022","DOI":"10.18653\/v1\/2022.naacl-tutorials.5"},{"key":"51_CR5","unstructured":"Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: ICML (2011)"},{"key":"51_CR6","doi-asserted-by":"crossref","unstructured":"Conneau, A., Schwenk, H., Barrault, L., Lecun, Y.: Very deep convolutional networks for text classification. arXiv preprint arXiv:1606.01781 (2016)","DOI":"10.18653\/v1\/E17-1104"},{"key":"51_CR7","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"51_CR8","unstructured":"Zhang, Z., Bizer, C., Peeters, R., Primpeli, A.: MWPD 2020: semantic web challenge on mining the web of html-embedded product data. In: MWPD@ ISWC (2020)"},{"issue":"19","key":"51_CR9","doi-asserted-by":"publisher","first-page":"25941","DOI":"10.1007\/s11042-018-5829-4","volume":"77","author":"S Wazarkar","year":"2018","unstructured":"Wazarkar, S., Keshavamurthy, B.N.: Fashion image classification using matching points with linear convolution. Multimedia Tools Appl. 77(19), 25941\u201325958 (2018). https:\/\/doi.org\/10.1007\/s11042-018-5829-4","journal-title":"Multimedia Tools Appl."},{"key":"51_CR10","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.inffus.2019.08.009","volume":"55","author":"W Zhang","year":"2020","unstructured":"Zhang, W., Yu, J., Hu, H., Hu, H., Qin, Z.: Multimodal feature fusion by relational reasoning and attention for visual question answering. Inf. Fusion 55, 116\u2013126 (2020)","journal-title":"Inf. Fusion"},{"key":"51_CR11","doi-asserted-by":"crossref","unstructured":"Misikir Tashu, T., Fattouh, S., Kiss, P., Horvath, T.: Multimodal e-commerce product classification using hierarchical fusion. arXiv e-prints (2022) arXiv-2207","DOI":"10.1109\/CITDS54976.2022.9914136"},{"key":"51_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1007\/978-3-319-70139-4_22","volume-title":"Neural Information Processing","author":"L Li","year":"2017","unstructured":"Li, L., Nie, Y., Han, W., Huang, J.: A multi-attention-based bidirectional long short-term memory network for relation extraction. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.-S.M. (eds.) ICONIP 2017. LNCS, vol. 10638, pp. 216\u2013227. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-70139-4_22"},{"key":"51_CR13","unstructured":"Chen, D., Liu, F., Du, X., Gao, R., Xu, F.: MEP-3M: a large-scale multi-modal e-commerce products dataset"},{"key":"51_CR14","doi-asserted-by":"crossref","unstructured":"Liu, Z., Shen, Y., Lakshminarasimhan, V.B., Liang, P.P., Zadeh, A., Morency, L.P.: Efficient low-rank multimodal fusion with modality-specific factors. arXiv preprint arXiv:1806.00064 (2018)","DOI":"10.18653\/v1\/P18-1209"},{"key":"51_CR15","doi-asserted-by":"crossref","unstructured":"Zadeh, A., Chen, M., Poria, S., Cambria, E., Morency, L.P.: Tensor fusion network for multimodal sentiment analysis. arXiv preprint arXiv:1707.07250 (2017)","DOI":"10.18653\/v1\/D17-1115"}],"container-title":["Lecture Notes in Computer Science","Biometric Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20233-9_51","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,11]],"date-time":"2023-03-11T01:07:42Z","timestamp":1678496862000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20233-9_51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031202322","9783031202339"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20233-9_51","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"3 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCBR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Biometric Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"27 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 October 2022","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":"ccbr2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ccbr99.cn\/","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":"115","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":"70","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":"61% - 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":"3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}