{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T20:39:49Z","timestamp":1742935189458,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031463075"},{"type":"electronic","value":"9783031463082"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-46308-2_28","type":"book-chapter","created":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T18:01:24Z","timestamp":1698602484000},"page":"335-347","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-modal Context-Aware Network for\u00a0Scene Graph Generation"],"prefix":"10.1007","author":[{"given":"Junjie","family":"Ye","sequence":"first","affiliation":[]},{"given":"Bing-Kun","family":"Bao","sequence":"additional","affiliation":[]},{"given":"Zhiyi","family":"Tan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,30]]},"reference":[{"key":"28_CR1","doi-asserted-by":"crossref","unstructured":"Chen, T., et al.: Knowledge-embedded routing network for scene graph generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6163\u20136171 (2019)","DOI":"10.1109\/CVPR.2019.00632"},{"key":"28_CR2","doi-asserted-by":"crossref","unstructured":"Cui, Z., et al.: Context-dependent diffusion network for visual relationship detection. In: Proceedings of the ACM International Conference on Multimedia, pp. 1475\u20131482 (2018)","DOI":"10.1145\/3240508.3240668"},{"issue":"1","key":"28_CR3","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1007\/s11263-016-0981-7","volume":"123","author":"R Krishna","year":"2017","unstructured":"Krishna, R., et al.: Visual genome: connecting language and vision using crowdsourced dense image annotations. Int. J. Comput. Vis. 123(1), 32\u201373 (2017)","journal-title":"Int. J. Comput. Vis."},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Lin, X., et al.: GPS-net: graph property sensing network for scene graph generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3746\u20133753 (2020)","DOI":"10.1109\/CVPR42600.2020.00380"},{"issue":"11","key":"28_CR5","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/219717.219748","volume":"38","author":"GA Miller","year":"1995","unstructured":"Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39\u201341 (1995)","journal-title":"Commun. ACM"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Pennington, J., et al.: GloVe: global vectors for word representation. In: Proceedings of the conference on Empirical Methods in Natural Language Processing, pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Qi, M., et al.: Attentive relational networks for mapping images to scene graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3957\u20133966 (2019)","DOI":"10.1109\/CVPR.2019.00408"},{"key":"28_CR8","unstructured":"Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. Proceedings of the Advances in Neural Information Processing Systems, vol. 28, pp. 91\u201399 (2015)"},{"key":"28_CR9","doi-asserted-by":"crossref","unstructured":"Sharifzadeh, S., et al.: Classification by attention: scene graph classification with prior knowledge. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 5025\u20135033 (2021)","DOI":"10.1609\/aaai.v35i6.16636"},{"key":"28_CR10","series-title":"Theory and Applications of Natural Language Processing","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/978-3-642-35085-6_6","volume-title":"The People\u2019s Web Meets NLP","author":"R Speer","year":"2013","unstructured":"Speer, R., Havasi, C.: ConceptNet 5: a large semantic network for relational knowledge. In: Gurevych, I., Kim, J. (eds.) The People\u2019s Web Meets NLP. TANLP, pp. 161\u2013176. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-35085-6_6"},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Suhail, M., et al.: Energy-based learning for scene graph generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 13936\u201313945 (2021)","DOI":"10.1109\/CVPR46437.2021.01372"},{"key":"28_CR12","doi-asserted-by":"crossref","unstructured":"Xu, D., et al.: Scene graph generation by iterative message passing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5410\u20135419 (2017)","DOI":"10.1109\/CVPR.2017.330"},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"Yan, S., et al.: PCPL: predicate-correlation perception learning for unbiased scene graph generation. In: Proceedings of the ACM International Conference on Multimedia, pp. 265\u2013273 (2020)","DOI":"10.1145\/3394171.3413722"},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Yang, G., et al.: Probabilistic modeling of semantic ambiguity for scene graph generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12527\u201312536 (2021)","DOI":"10.1109\/CVPR46437.2021.01234"},{"key":"28_CR15","unstructured":"Yun, S., et al.: Graph transformer networks. In: Proceedings of the Advances in Neural Information Processing Systems, vol. 32, pp. 11983\u201311993 (2019)"},{"key":"28_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1007\/978-3-030-58592-1_36","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Zareian","year":"2020","unstructured":"Zareian, A., Karaman, S., Chang, S.-F.: Bridging knowledge graphs to generate scene graphs. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12368, pp. 606\u2013623. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58592-1_36"},{"key":"28_CR17","doi-asserted-by":"crossref","unstructured":"Zellers, R., et al.: Neural motifs: scene graph parsing with global context. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5831\u20135840 (2018)","DOI":"10.1109\/CVPR.2018.00611"},{"key":"28_CR18","doi-asserted-by":"crossref","unstructured":"Zhong, Y., et al.: Learning to generate scene graph from natural language supervision. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1823\u20131834 (2021)","DOI":"10.1109\/ICCV48922.2021.00184"}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46308-2_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T18:05:20Z","timestamp":1698602720000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46308-2_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031463075","9783031463082"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46308-2_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icig2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icig2023.csig.org.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":"Conference Management Toolkit","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"409","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":"166","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":"41% - 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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}