{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T14:22:26Z","timestamp":1772893346488,"version":"3.50.1"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031154706","type":"print"},{"value":"9783031154713","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\/978-3-031-15471-3_38","type":"book-chapter","created":{"date-parts":[[2022,9,11]],"date-time":"2022-09-11T09:04:42Z","timestamp":1662887082000},"page":"443-454","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Neuro-Symbolic AI System for Visual Question Answering in Pedestrian Video Sequences"],"prefix":"10.1007","author":[{"given":"Jaeil","family":"Park","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seok-Jun","family":"Bu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sung-Bae","family":"Cho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,12]]},"reference":[{"key":"38_CR1","doi-asserted-by":"crossref","unstructured":"Park, K.-W., Bu, S.-J., Cho, S.-B.: Evolutionary optimization of neuro-symbolic integration for phishing URL detection. In: International Conference on Hybrid Artificial Intelligent Systems, pp. 88\u2013100 (2021)","DOI":"10.1007\/978-3-030-86271-8_8"},{"key":"38_CR2","unstructured":"Yi, K., Wu, J., Gan, C., Torralba, A., Kohli, P., Tenenbaum, J.: Neural-symbolic VQA: disentangling reasoning from vision and language understanding. In: Advances in Neural Information Processing Systems, pp. 1031\u20131042 (2018)"},{"key":"38_CR3","unstructured":"Amizadeh, S., Palangi, H., Polozov, O., Huang, Y., Kishida, K.: Neuro-symbolic visual reasoning: disentangling \u2018visual\u2019 from \u2018reasoning\u2019. In: International Conference on Machine Learning, pp. 279\u2013290 (2020)"},{"key":"38_CR4","doi-asserted-by":"crossref","unstructured":"Shi, J., Zhang, H., Li, J.: Explainable and Explicit Visual Reasoning over Scene Graphs. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 8368\u20138376 (2019)","DOI":"10.1109\/CVPR.2019.00857"},{"key":"38_CR5","doi-asserted-by":"publisher","first-page":"1367","DOI":"10.1109\/TPAMI.2017.2708709","volume":"40","author":"P Wang","year":"2018","unstructured":"Wang, P., Wu, Q., Shen, C., Dick, A., Van Den Hengel, A.: FVQA: fact-based visual question answering. IEEE Trans. Pattern Anal. Mach. Intell. 40, 1367\u20131381 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"38_CR6","doi-asserted-by":"crossref","unstructured":"Teney, D., Liu, L., van Den Hengel, A.: Graph-structured representations for visual question answering. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2017)","DOI":"10.1109\/CVPR.2017.344"},{"key":"38_CR7","doi-asserted-by":"crossref","unstructured":"Song, Y.-S., Cho, S.-B.: Objects relationship modeling for improving object detection of service robots using Bayesian network integration. In: International Conference on Intelligent Computing, pp. 678\u2013683 (2006)","DOI":"10.1007\/11816157_126"},{"key":"38_CR8","unstructured":"Mao, J., Gan, C., Deepmind, P.K., Tenenbaum, J.B., Wu, J.: The neuro-symbolic concept learner: interpreting scenes, words, and sentences from natural supervision. In: International Conference on Learning Representations (2019)"},{"key":"38_CR9","unstructured":"Han, C., Mao, J., Gan, C., Tenenbaum, J.B., Wu, J.: Visual concept metaconcept learning. In: Advances in Neural Information Processing Systems, pp. 5001\u20135012 (2019)"},{"key":"38_CR10","doi-asserted-by":"publisher","first-page":"3196","DOI":"10.1109\/TMM.2020.2972830","volume":"22","author":"J Yu","year":"2020","unstructured":"Yu, J., et al.: Reasoning on the relation: enhancing visual representation for visual question answering and cross-modal retrieval. IEEE Trans. Multimedia 22, 3196\u20133209 (2020)","journal-title":"IEEE Trans. Multimedia"},{"issue":"1","key":"38_CR11","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1007\/s11263-016-0966-6","volume":"123","author":"A Agrawal","year":"2016","unstructured":"Agrawal, A., et al.: VQA: visual question answering. Int. J. Comput. Vision 123(1), 4\u201331 (2016). https:\/\/doi.org\/10.1007\/s11263-016-0966-6","journal-title":"Int. J. Comput. Vision"},{"key":"38_CR12","doi-asserted-by":"crossref","unstructured":"Hu, R., Andreas, J., Rohrbach, M., Darrell, T., Saenko, K.: Learning to reason: end-to-end module networks for visual question answering. In: IEEE International Conference on Computer Vision, pp. 804\u2013813 (2017)","DOI":"10.1109\/ICCV.2017.93"},{"key":"38_CR13","unstructured":"Cong, W., Wang, W., Lee, W.-C.: Scene Graph Generation via Conditional Random Fields. arXiv preprint arXiv:1811.08075 (2018)"},{"key":"38_CR14","doi-asserted-by":"crossref","unstructured":"Kolesnikov, A., Kuznetsova, A., Lampert, C., Ferrari, V.: Detecting visual relationships using box attention. In: IEEE International Conference on Computer Vision Workshops, pp. 1749\u20131753 (2019)","DOI":"10.1109\/ICCVW.2019.00217"},{"key":"38_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1007\/978-3-030-01219-9_20","volume-title":"Computer Vision \u2013 ECCV 2018","author":"G Yin","year":"2018","unstructured":"Yin, G., et al.: Zoom-net: mining deep feature interactions for visual relationship recognition. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 330\u2013347. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01219-9_20"},{"key":"38_CR16","doi-asserted-by":"crossref","unstructured":"Tang, K., Zhang, H., Wu, B., Luo, W., Liu, W.: Learning to compose dynamic tree structures for visual contexts. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6619\u20136628 (2019)","DOI":"10.1109\/CVPR.2019.00678"},{"key":"38_CR17","doi-asserted-by":"crossref","unstructured":"Goller, C., Kuchler, A.: Learning task-dependent distributed representations by backpropagation through structure. In: International Conference on Neural Networks, pp. 347\u2013352 (1996)","DOI":"10.1109\/ICNN.1996.548916"},{"key":"38_CR18","doi-asserted-by":"crossref","unstructured":"Gori, M., Monfardini, G., Scarselli, F.: A new model for learning in graph domains. In: IEEE International Joint Conference on Neural Networks, pp.729\u2013734 (2005)","DOI":"10.1109\/IJCNN.2005.1555942"},{"key":"38_CR19","doi-asserted-by":"crossref","unstructured":"Li, Y., Ouyang, W., Zhou, B., Shi, J., Zhang, C., Wang, X.: Factorizable net: an efficient subgraph-based framework for scene graph generation. In: European Conference on Computer Vision, pp. 346\u2013363 (2018)","DOI":"10.1007\/978-3-030-01246-5_21"},{"key":"38_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1007\/978-3-030-01246-5_41","volume-title":"Computer Vision \u2013 ECCV 2018","author":"J Yang","year":"2018","unstructured":"Yang, J., Lu, J., Lee, S., Batra, D., Parikh, D.: Graph R-CNN for scene graph generation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 690\u2013706. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01246-5_41"},{"issue":"6","key":"38_CR21","doi-asserted-by":"publisher","first-page":"2050034","DOI":"10.1142\/S0129065720500343","volume":"40","author":"W-S Shin","year":"2020","unstructured":"Shin, W.-S., Bu, S.-J., Cho, S.-B.: 3D-convolutional neural network with generative adversarial network and autoencoder for robust anomaly detection in video surveillance. Int. J. Neural Syst. 40(6), 2050034 (2020)","journal-title":"Int. J. Neural Syst."}],"container-title":["Lecture Notes in Computer Science","Hybrid Artificial Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-15471-3_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T17:01:44Z","timestamp":1727974904000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-15471-3_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031154706","9783031154713"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-15471-3_38","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":"12 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Hybrid Artificial Intelligence Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salamancaa","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"5 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 September 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":"hais2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2022.haisconference.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"67","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":"43","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":"64% - 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":"2","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)"}}]}}