{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:13:46Z","timestamp":1775229226662,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030377335","type":"print"},{"value":"9783030377342","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T00:00:00Z","timestamp":1577145600000},"content-version":"tdm","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\/978-3-030-37734-2_40","type":"book-chapter","created":{"date-parts":[[2019,12,26]],"date-time":"2019-12-26T19:03:00Z","timestamp":1577386980000},"page":"489-501","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Relation Modeling with Graph Convolutional Networks for Facial Action Unit Detection"],"prefix":"10.1007","author":[{"given":"Zhilei","family":"Liu","sequence":"first","affiliation":[]},{"given":"Jiahui","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Cuicui","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Longbiao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jianwu","family":"Dang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,24]]},"reference":[{"key":"40_CR1","unstructured":"Atwood, J., Towsley, D.: Diffusion-convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1993\u20132001 (2016)"},{"key":"40_CR2","unstructured":"Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. arXiv preprint \narXiv:1312.6203\n\n (2013)"},{"key":"40_CR3","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, pp. 3844\u20133852 (2016)"},{"key":"40_CR4","unstructured":"Duvenaud, D.K., et al.: Convolutional networks on graphs for learning molecular fingerprints. In: Advances in Neural Information Processing Systems, pp. 2224\u20132232 (2015)"},{"key":"40_CR5","volume-title":"What the Face Reveals: Basic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS)","author":"R Ekman","year":"1997","unstructured":"Ekman, R.: What the Face Reveals: Basic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS). Oxford University Press, Oxford (1997)"},{"issue":"Aug","key":"40_CR6","first-page":"1871","volume":"9","author":"RE Fan","year":"2008","unstructured":"Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: a library for large linear classification. J. Mach. Learn. Res. 9(Aug), 1871\u20131874 (2008)","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"40_CR7","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.acha.2010.04.005","volume":"30","author":"DK Hammond","year":"2011","unstructured":"Hammond, D.K., Vandergheynst, P., Gribonval, R.: Wavelets on graphs via spectral graph theory. Appl. Comput. Harmonic Anal. 30(2), 129\u2013150 (2011)","journal-title":"Appl. Comput. Harmonic Anal."},{"key":"40_CR8","unstructured":"Henaff, M., Bruna, J., LeCun, Y.: Deep convolutional networks on graph-structured data. arXiv preprint \narXiv:1506.05163\n\n (2015)"},{"key":"40_CR9","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint \narXiv:1609.02907\n\n (2016)"},{"key":"40_CR10","doi-asserted-by":"publisher","first-page":"8594","DOI":"10.1609\/aaai.v33i01.33018594","volume":"33","author":"Guanbin Li","year":"2019","unstructured":"Li, G., Zhu, X., Zeng, Y., Wang, Q., Lin, L.: Semantic relationships guided representation learning for facial action unit recognition. arXiv preprint \narXiv:1904.09939\n\n (2019)","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"40_CR11","doi-asserted-by":"crossref","unstructured":"Li, W., Abtahi, F., Zhu, Z., Yin, L.: EAC-Net: a region-based deep enhancing and cropping approach for facial action unit detection. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp. 103\u2013110. IEEE (2017)","DOI":"10.1109\/FG.2017.136"},{"key":"40_CR12","unstructured":"Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.: Gated graph sequence neural networks. arXiv preprint \narXiv:1511.05493\n\n (2015)"},{"key":"40_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Z., Song, G., Cai, J., Cham, T.J., Zhang, J.: Conditional adversarial synthesis of 3D facial action units. arXiv preprint \narXiv:1802.07421\n\n (2018)","DOI":"10.1016\/j.neucom.2019.05.003"},{"key":"40_CR14","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1109\/TAFFC.2017.2731763","volume":"10","author":"B Martinez","year":"2017","unstructured":"Martinez, B., Valstar, M.F., Jiang, B., Pantic, M.: Automatic analysis of facial actions: a survey. IEEE Trans. Affect. Comput. 10, 325\u2013347 (2017)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"40_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/978-3-642-21735-7_7","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2011","author":"J Masci","year":"2011","unstructured":"Masci, J., Meier, U., Cire\u015fan, D., Schmidhuber, J.: Stacked convolutional auto-encoders for hierarchical feature extraction. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds.) ICANN 2011. LNCS, vol. 6791, pp. 52\u201359. Springer, Heidelberg (2011). \nhttps:\/\/doi.org\/10.1007\/978-3-642-21735-7_7"},{"issue":"2","key":"40_CR16","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1109\/T-AFFC.2013.4","volume":"4","author":"SM Mavadati","year":"2013","unstructured":"Mavadati, S.M., Mahoor, M.H., Bartlett, K., Trinh, P., Cohn, J.F.: DISFA: a spontaneous facial action intensity database. IEEE Trans. Affect. Comput. 4(2), 151\u2013160 (2013)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"40_CR17","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"40_CR18","unstructured":"Ng, Y.C., Colombo, N., Silva, R.: Bayesian semi-supervised learning with graph gaussian processes. In: Advances in Neural Information Processing Systems, pp. 1683\u20131694 (2018)"},{"key":"40_CR19","unstructured":"Niepert, M., Ahmed, M., Kutzkov, K.: Learning convolutional neural networks for graphs. In: International Conference on Machine Learning, pp. 2014\u20132023 (2016)"},{"key":"40_CR20","doi-asserted-by":"crossref","unstructured":"Song, Y., McDuff, D., Vasisht, D., Kapoor, A.: Exploiting sparsity and co-occurrence structure for action unit recognition. In: 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG). vol. 1, pp. 1\u20138. IEEE (2015)","DOI":"10.1109\/FG.2015.7163081"},{"key":"40_CR21","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701\u20131708 (2014)","DOI":"10.1109\/CVPR.2014.220"},{"issue":"3","key":"40_CR22","doi-asserted-by":"publisher","first-page":"1428","DOI":"10.1109\/TIP.2018.2878339","volume":"28","author":"S Wang","year":"2019","unstructured":"Wang, S., Hao, L., Ji, Q.: Facial action unit recognition and intensity estimation enhanced through label dependencies. IEEE Trans. Image Process. 28(3), 1428\u20131442 (2019). \nhttps:\/\/doi.org\/10.1109\/TIP.2018.2878339","journal-title":"IEEE Trans. Image Process."},{"key":"40_CR23","doi-asserted-by":"crossref","unstructured":"Wang, Z., Li, Y., Wang, S., Qiang, J.: Capturing global semantic relationships for facial action unit recognition. In: IEEE International Conference on Computer Vision (2014)","DOI":"10.1109\/ICCV.2013.410"},{"key":"40_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: A high-resolution spontaneous 3D dynamic facial expression database. In: 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1\u20136. IEEE (2013)","DOI":"10.1109\/FG.2013.6553788"},{"issue":"8","key":"40_CR25","doi-asserted-by":"publisher","first-page":"3931","DOI":"10.1109\/TIP.2016.2570550","volume":"25","author":"K Zhao","year":"2016","unstructured":"Zhao, K., Chu, W.S., De la Torre, F., Cohn, J.F., Zhang, H.: Joint patch and multi-label learning for facial action unit and holistic expression recognition. IEEE Trans. Image Process. 25(8), 3931\u20133946 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"40_CR26","doi-asserted-by":"crossref","unstructured":"Zhao, K., Chu, W.S., Zhang, H.: Deep region and multi-label learning for facial action unit detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3391\u20133399 (2016)","DOI":"10.1109\/CVPR.2016.369"},{"issue":"1","key":"40_CR27","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1093\/bioinformatics\/bth463","volume":"21","author":"M Zou","year":"2005","unstructured":"Zou, M., Conzen, S.D.: A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics 21(1), 71\u201379 (2005)","journal-title":"Bioinformatics"}],"container-title":["Lecture Notes in Computer Science","MultiMedia Modeling"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-37734-2_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,2,6]],"date-time":"2020-02-06T13:12:02Z","timestamp":1580994722000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-37734-2_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,24]]},"ISBN":["9783030377335","9783030377342"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-37734-2_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,24]]},"assertion":[{"value":"24 December 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MMM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Multimedia Modeling","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","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":"5 January 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 January 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mmm2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.mmm2020.kr\/","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":"171","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":"40","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":"23% - 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":"5","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":"Of the 171 submissions, 46 were accepted as poster papers; of the 49 special session paper submissions, 28 were accepted for oral presentation and 8 for poster presentation; 9 demo papers and 10 VBS papers were also accepted.","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)"}}]}}