{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T16:24:17Z","timestamp":1790353457989,"version":"4.1.0"},"publisher-location":"New York, NY, USA","reference-count":52,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"EPSRC","award":["EP\/R513106\/1"],"award-info":[{"award-number":["EP\/R513106\/1"]}]},{"name":"EU H2020 AI4Media","award":["951911"],"award-info":[{"award-number":["951911"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,10]]},"DOI":"10.1145\/3503161.3548373","type":"proceedings-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T15:43:01Z","timestamp":1665416581000},"page":"80-89","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Learning from Label Relationships in Human Affect"],"prefix":"10.1145","author":[{"given":"Niki Maria","family":"Foteinopoulou","sequence":"first","affiliation":[{"name":"Queen Mary University of London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ioannis","family":"Patras","sequence":"additional","affiliation":[{"name":"Queen Mary University of London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.122"},{"key":"e_1_3_2_2_2_1","volume-title":"Diagnostic and Statistical Manual of Mental Disorders","author":"American Psychiatric Association","unstructured":"American Psychiatric Association . 2013. Diagnostic and Statistical Manual of Mental Disorders ( fifth edition ed.). American Psychiatric Association . https:\/\/doi.org\/10.1176\/appi.books.9780890425596 American Psychiatric Association. 2013. Diagnostic and Statistical Manual of Mental Disorders (fifth edition ed.). American Psychiatric Association. https:\/\/doi.org\/10.1176\/appi.books.9780890425596"},{"key":"e_1_3_2_2_3_1","volume-title":"The OMG-Emotion Behavior Dataset. In 2018 International Joint Conference on Neural Networks (IJCNN). 1--7. https:\/\/doi.org\/10","author":"Barros Pablo","year":"2018","unstructured":"Pablo Barros , Nikhil Churamani , Egor Lakomkin , Henrique Siqueira , Alexander Sutherland , and Stefan Wermter . 2018 . The OMG-Emotion Behavior Dataset. In 2018 International Joint Conference on Neural Networks (IJCNN). 1--7. https:\/\/doi.org\/10 .1109\/IJCNN.2018.8489099 ISSN: 2161--4407. Pablo Barros, Nikhil Churamani, Egor Lakomkin, Henrique Siqueira, Alexander Sutherland, and Stefan Wermter. 2018. The OMG-Emotion Behavior Dataset. In 2018 International Joint Conference on Neural Networks (IJCNN). 1--7. https:\/\/doi.org\/10.1109\/IJCNN.2018.8489099 ISSN: 2161--4407."},{"key":"e_1_3_2_2_4_1","volume-title":"SchiNet: Automatic Estimation of Symptoms of Schizophrenia from Facial Behaviour Analysis","author":"Bishay Mina","year":"2018","unstructured":"Mina Bishay , Petar Palasek , Stefan Priebe , and Ioannis Patras . 2018. SchiNet: Automatic Estimation of Symptoms of Schizophrenia from Facial Behaviour Analysis . IEEE Transactions on Affective Computing ( 2018 ). https:\/\/doi.org\/10.1109\/TAFFC.2019.2907628 Mina Bishay, Petar Palasek, Stefan Priebe, and Ioannis Patras. 2018. SchiNet: Automatic Estimation of Symptoms of Schizophrenia from Facial Behaviour Analysis. IEEE Transactions on Affective Computing (2018). https:\/\/doi.org\/10.1109\/TAFFC.2019.2907628"},{"key":"e_1_3_2_2_5_1","volume-title":"Automatic Facial Expression Analysis in Diagnosis and Treatment of Schizophrenia. Thesis","author":"Thabet Bishay Mina Adel","unstructured":"Mina Adel Thabet Bishay . 2020. Automatic Facial Expression Analysis in Diagnosis and Treatment of Schizophrenia. Thesis . Queen Mary University of London . https:\/\/qmro.qmul.ac.uk\/xmlui\/handle\/123456789\/69449 Accepted: 2020-12-18T16:23:10Z. Mina Adel Thabet Bishay. 2020. Automatic Facial Expression Analysis in Diagnosis and Treatment of Schizophrenia. Thesis. Queen Mary University of London. https:\/\/qmro.qmul.ac.uk\/xmlui\/handle\/123456789\/69449 Accepted: 2020-12-18T16:23:10Z."},{"key":"e_1_3_2_2_6_1","volume-title":"Pre-training strategies and datasets for facial representation learning. arXiv:2103.16554 [cs] (March","author":"Bulat Adrian","year":"2021","unstructured":"Adrian Bulat , Shiyang Cheng , Jing Yang , Andrew Garbett , Enrique Sanchez , and Georgios Tzimiropoulos . 2021. Pre-training strategies and datasets for facial representation learning. arXiv:2103.16554 [cs] (March 2021 ). http:\/\/arxiv.org\/abs\/2103.16554 arXiv: 2103.16554. Adrian Bulat, Shiyang Cheng, Jing Yang, Andrew Garbett, Enrique Sanchez, and Georgios Tzimiropoulos. 2021. Pre-training strategies and datasets for facial representation learning. arXiv:2103.16554 [cs] (March 2021). http:\/\/arxiv.org\/abs\/2103.16554 arXiv: 2103.16554."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/FG.2018.00020"},{"key":"e_1_3_2_2_8_1","volume-title":"A Deep Multiscale Spatiotemporal Network for Assessing Depression from Facial Dynamics","author":"de Melo Wheidima Carneiro","year":"2020","unstructured":"Wheidima Carneiro de Melo , Eric Granger , and Abdenour Hadid . 2020. A Deep Multiscale Spatiotemporal Network for Assessing Depression from Facial Dynamics . IEEE Transactions on Affective Computing ( 2020 ), 1-1. https:\/\/doi.org\/10.1109\/TAFFC.2020.3021755 Wheidima Carneiro de Melo, Eric Granger, and Abdenour Hadid. 2020. A Deep Multiscale Spatiotemporal Network for Assessing Depression from Facial Dynamics. IEEE Transactions on Affective Computing (2020), 1-1. https:\/\/doi.org\/10.1109\/TAFFC.2020.3021755"},{"key":"e_1_3_2_2_9_1","volume-title":"A Short Note about Kinetics-600. arXiv:1808.01340 [cs] (Aug","author":"Carreira Joao","year":"2018","unstructured":"Joao Carreira , Eric Noland , Andras Banki-Horvath , Chloe Hillier , and Andrew Zisserman . 2018. A Short Note about Kinetics-600. arXiv:1808.01340 [cs] (Aug . 2018 ). http:\/\/arxiv.org\/abs\/1808.01340 arXiv: 1808.01340. Joao Carreira, Eric Noland, Andras Banki-Horvath, Chloe Hillier, and Andrew Zisserman. 2018. A Short Note about Kinetics-600. arXiv:1808.01340 [cs] (Aug. 2018). http:\/\/arxiv.org\/abs\/1808.01340 arXiv: 1808.01340."},{"key":"e_1_3_2_2_10_1","volume-title":"International conference on machine learning. PMLR, 1597--1607","author":"Chen Ting","year":"2020","unstructured":"Ting Chen , Simon Kornblith , Mohammad Norouzi , and Geoffrey Hinton . 2020 . A simple framework for contrastive learning of visual representations . In International conference on machine learning. PMLR, 1597--1607 . Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597--1607."},{"key":"e_1_3_2_2_11_1","volume-title":"Big self-supervised models are strong semi-supervised learners. Advances in neural information processing systems 33","author":"Chen Ting","year":"2020","unstructured":"Ting Chen , Simon Kornblith , Kevin Swersky , Mohammad Norouzi , and Geoffrey E Hinton . 2020. Big self-supervised models are strong semi-supervised learners. Advances in neural information processing systems 33 ( 2020 ), 22243--22255. Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E Hinton. 2020. Big self-supervised models are strong semi-supervised learners. Advances in neural information processing systems 33 (2020), 22243--22255."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i03.5646"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"crossref","unstructured":"J. Deng W. Dong R. Socher L.-J. Li K. Li and L. Fei-Fei. 2009. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR09.  J. Deng W. Dong R. Socher L.-J. Li K. Li and L. Fei-Fei. 2009. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR09.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_2_14_1","volume-title":"Friesen","author":"Ekman Paul","year":"1978","unstructured":"Paul Ekman and Wallace V . Friesen . 1978 . Facial action coding system: Investigator's guide. Consulting Psychologists Press . Paul Ekman and Wallace V. Friesen. 1978. Facial action coding system: Investigator's guide. Consulting Psychologists Press."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.schres.2010.08.039"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"crossref","unstructured":"Niki Maria Foteinopoulou Christos Tzelepis and Ioannis Patras. 2021. Estimating continuous affect with uncertainty. Nara Japan. https:\/\/doi.org\/10.1109\/ACII52823.2021.9597425  Niki Maria Foteinopoulou Christos Tzelepis and Ioannis Patras. 2021. Estimating continuous affect with uncertainty. Nara Japan. https:\/\/doi.org\/10.1109\/ACII52823.2021.9597425","DOI":"10.1109\/ACII52823.2021.9597425"},{"key":"e_1_3_2_2_17_1","volume-title":"Proceedings of the fourteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 315--323","author":"Glorot Xavier","year":"2011","unstructured":"Xavier Glorot , Antoine Bordes , and Yoshua Bengio . 2011 . Deep sparse rectifier neural networks . In Proceedings of the fourteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 315--323 . Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011. Deep sparse rectifier neural networks. In Proceedings of the fourteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 315--323."},{"key":"e_1_3_2_2_18_1","unstructured":"Ian J. Goodfellow Dumitru Erhan Pierre Luc Carrier Aaron Courville Mehdi Mirza Ben Hamner Will Cukierski Yichuan Tang David Thaler Dong-Hyun Lee Yingbo Zhou Chetan Ramaiah Fangxiang Feng Ruifan Li Xiaojie Wang Dimitris Athanasakis John Shawe-Taylor Maxim Milakov John Park Radu Ionescu Marius Popescu Cristian Grozea James Bergstra Jingjing Xie Lukasz Romaszko Bing Xu Zhang Chuang and Yoshua Bengio. 2013. Challenges in Representation Learning: A report on three machine learning contests. arXiv:1307.0414 [cs stat] (July 2013). http:\/\/arxiv.org\/abs\/1307.0414 arXiv: 1307.0414.  Ian J. Goodfellow Dumitru Erhan Pierre Luc Carrier Aaron Courville Mehdi Mirza Ben Hamner Will Cukierski Yichuan Tang David Thaler Dong-Hyun Lee Yingbo Zhou Chetan Ramaiah Fangxiang Feng Ruifan Li Xiaojie Wang Dimitris Athanasakis John Shawe-Taylor Maxim Milakov John Park Radu Ionescu Marius Popescu Cristian Grozea James Bergstra Jingjing Xie Lukasz Romaszko Bing Xu Zhang Chuang and Yoshua Bengio. 2013. Challenges in Representation Learning: A report on three machine learning contests. arXiv:1307.0414 [cs stat] (July 2013). http:\/\/arxiv.org\/abs\/1307.0414 arXiv: 1307.0414."},{"key":"e_1_3_2_2_19_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep Residual Learning for Image Recognition. 770--778. https:\/\/openaccess.thecvf.com\/content_cvpr_2016\/html\/He_Deep_Residual_Learning_CVPR_2016_paper.html  Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep Residual Learning for Image Recognition. 770--778. https:\/\/openaccess.thecvf.com\/content_cvpr_2016\/html\/He_Deep_Residual_Learning_CVPR_2016_paper.html"},{"key":"e_1_3_2_2_20_1","volume-title":"ACM Multimedia Asia (MMAsia '21)","author":"Hirata Tomu","unstructured":"Tomu Hirata , Yusuke Mukuta , and Tatsuya Harada . 2021. Making Video Recognition Models Robust to Common Corruptions With Supervised Contrastive Learning . In ACM Multimedia Asia (MMAsia '21) . Association for Computing Machinery , New York, NY, USA , 1--6. https:\/\/doi.org\/10.1145\/3469877.3497692 Tomu Hirata, Yusuke Mukuta, and Tatsuya Harada. 2021. Making Video Recognition Models Robust to Common Corruptions With Supervised Contrastive Learning. In ACM Multimedia Asia (MMAsia '21). Association for Computing Machinery, New York, NY, USA, 1--6. https:\/\/doi.org\/10.1145\/3469877.3497692"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2022.3163777"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12193-013-0123-2"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/FG.2013.6553796"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1093\/schbul\/13.2.261"},{"key":"e_1_3_2_2_25_1","volume-title":"The Kinetics Human Action Video Dataset. arXiv:1705.06950 [cs] (May","author":"Kay Will","year":"2017","unstructured":"Will Kay , Joao Carreira , Karen Simonyan , Brian Zhang , Chloe Hillier , Sudheendra Vijayanarasimhan , Fabio Viola , Tim Green , Trevor Back , Paul Natsev , Mustafa Suleyman , and Andrew Zisserman . 2017. The Kinetics Human Action Video Dataset. arXiv:1705.06950 [cs] (May 2017 ). http:\/\/arxiv.org\/abs\/1705.06950 arXiv: 1705.06950. Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, Mustafa Suleyman, and Andrew Zisserman. 2017. The Kinetics Human Action Video Dataset. arXiv:1705.06950 [cs] (May 2017). http:\/\/arxiv.org\/abs\/1705.06950 arXiv: 1705.06950."},{"key":"e_1_3_2_2_26_1","volume-title":"Lin (Eds.)","volume":"33","author":"Khosla Prannay","year":"2020","unstructured":"Prannay Khosla , Piotr Teterwak , Chen Wang , Aaron Sarna , Yonglong Tian , Phillip Isola , Aaron Maschinot , Ce Liu , and Dilip Krishnan . 2020 . Supervised Contrastive Learning. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H . Lin (Eds.) , Vol. 33 . Curran Associates, Inc. , 18661--18673. https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/d89a66c7c80a29b1bdbab0f2a1a94af8-Paper.pdf Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. 2020. Supervised Contrastive Learning. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 18661--18673. https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/d89a66c7c80a29b1bdbab0f2a1a94af8-Paper.pdf"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i7.16743"},{"key":"e_1_3_2_2_28_1","volume-title":"A Multi-component CNN-RNN Approach for Dimensional Emotion Recognition in-the-wild. arXiv:1805.01452 [cs, eess, stat] (Dec","author":"Kollias Dimitrios","year":"2019","unstructured":"Dimitrios Kollias and Stefanos Zafeiriou . 2019. A Multi-component CNN-RNN Approach for Dimensional Emotion Recognition in-the-wild. arXiv:1805.01452 [cs, eess, stat] (Dec . 2019 ). http:\/\/arxiv.org\/abs\/1805.01452 arXiv: 1805.01452. Dimitrios Kollias and Stefanos Zafeiriou. 2019. A Multi-component CNN-RNN Approach for Dimensional Emotion Recognition in-the-wild. arXiv:1805.01452 [cs, eess, stat] (Dec. 2019). http:\/\/arxiv.org\/abs\/1805.01452 arXiv: 1805.01452."},{"key":"e_1_3_2_2_29_1","volume-title":"Temporal Modeling Approaches for Largescale Youtube-8M Video Understanding. arXiv:1707.04555 [cs] (July","author":"Li Fu","year":"2017","unstructured":"Fu Li , Chuang Gan , Xiao Liu , Yunlong Bian , Xiang Long , Yandong Li , Zhichao Li , Jie Zhou , and Shilei Wen . 2017. Temporal Modeling Approaches for Largescale Youtube-8M Video Understanding. arXiv:1707.04555 [cs] (July 2017 ). http:\/\/arxiv.org\/abs\/1707.04555 arXiv: 1707.04555. Fu Li, Chuang Gan, Xiao Liu, Yunlong Bian, Xiang Long, Yandong Li, Zhichao Li, Jie Zhou, and Shilei Wen. 2017. Temporal Modeling Approaches for Largescale Youtube-8M Video Understanding. arXiv:1707.04555 [cs] (July 2017). http:\/\/arxiv.org\/abs\/1707.04555 arXiv: 1707.04555."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2022.01.003"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3242969.3264992"},{"key":"e_1_3_2_2_32_1","volume-title":"Towards General Models of Player Experience: A Study Within Genres. In 2021 IEEE Conference on Games (CoG). 01--08","author":"Melhart David","year":"2021","unstructured":"David Melhart , Antonios Liapis , and Georgios N. Yannakakis . 2021 . Towards General Models of Player Experience: A Study Within Genres. In 2021 IEEE Conference on Games (CoG). 01--08 . https:\/\/doi.org\/10.1109\/CoG52621. 2021 .9618902 David Melhart, Antonios Liapis, and Georgios N. Yannakakis. 2021. Towards General Models of Player Experience: A Study Within Genres. In 2021 IEEE Conference on Games (CoG). 01--08. https:\/\/doi.org\/10.1109\/CoG52621.2021.9618902"},{"key":"e_1_3_2_2_33_1","volume-title":"Depression Detection Based on Deep Distribution Learning. In 2019 IEEE International Conference on Image Processing (ICIP). 4544--4548","author":"de Melo W. C.","year":"2019","unstructured":"W. C. de Melo , E. Granger , and A. Hadid . 2019 . Depression Detection Based on Deep Distribution Learning. In 2019 IEEE International Conference on Image Processing (ICIP). 4544--4548 . https:\/\/doi.org\/10.1109\/ICIP. 2019 .8803467 ISSN: 2381--8549. W. C. de Melo, E. Granger, and A. Hadid. 2019. Depression Detection Based on Deep Distribution Learning. In 2019 IEEE International Conference on Image Processing (ICIP). 4544--4548. https:\/\/doi.org\/10.1109\/ICIP.2019.8803467 ISSN: 2381--8549."},{"key":"e_1_3_2_2_34_1","volume-title":"Niculae Sebe, and Ioannis Patras.","author":"Miranda Correa Juan Abdon","year":"2018","unstructured":"Juan Abdon Miranda Correa , Mojtaba Khomami Abadi , Niculae Sebe, and Ioannis Patras. 2018 . AMIGOS : A Dataset for Affect, Personality and Mood Research on Individuals and Groups . IEEE Transactions on Affective Computing (2018), 1-1. https:\/\/doi.org\/10.1109\/TAFFC.2018.2884461 Juan Abdon Miranda Correa, Mojtaba Khomami Abadi, Niculae Sebe, and Ioannis Patras. 2018. AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups. IEEE Transactions on Affective Computing (2018), 1-1. https:\/\/doi.org\/10.1109\/TAFFC.2018.2884461"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3321509"},{"key":"e_1_3_2_2_36_1","volume-title":"A deep network for arousal-valence emotion prediction with acoustic-visual cues. arXiv preprint arXiv:1805.00638","author":"Peng Songyou","year":"2018","unstructured":"Songyou Peng , Le Zhang , Yutong Ban , Meng Fang , and Stefan Winkler . 2018. A deep network for arousal-valence emotion prediction with acoustic-visual cues. arXiv preprint arXiv:1805.00638 ( 2018 ). Songyou Peng, Le Zhang, Yutong Ban, Meng Fang, and Stefan Winkler. 2018. A deep network for arousal-valence emotion prediction with acoustic-visual cues. arXiv preprint arXiv:1805.00638 (2018)."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1192\/bjp.bp.115.171397"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00689"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1037\/h0077714"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2021.01.066"},{"key":"e_1_3_2_2_41_1","volume-title":"Eyre","author":"Smith Erin","year":"2020","unstructured":"Erin Smith , Eric A. Storch , Helen Lavretsky , Jeffrey L. Cummings , and Harris A . Eyre . 2020 . Affective Computing for Brain Health Disorders. Springer International Publishing , Cham, 1--14. https:\/\/doi.org\/10.1007\/978-3-319-75479-6_36-1 Erin Smith, Eric A. Storch, Helen Lavretsky, Jeffrey L. Cummings, and Harris A. Eyre. 2020. Affective Computing for Brain Health Disorders. Springer International Publishing, Cham, 1--14. https:\/\/doi.org\/10.1007\/978-3-319-75479-6_36-1"},{"key":"e_1_3_2_2_42_1","volume-title":"Estimation of continuous valence and arousal levels from faces in naturalistic conditions. Nature Machine Intelligence 3, 1 (Jan","author":"Toisoul Antoine","year":"2021","unstructured":"Antoine Toisoul , Jean Kossaifi , Adrian Bulat , Georgios Tzimiropoulos , and Maja Pantic . 2021. Estimation of continuous valence and arousal levels from faces in naturalistic conditions. Nature Machine Intelligence 3, 1 (Jan . 2021 ), 42--50. https:\/\/doi.org\/10.1038\/s42256-020-00280-0 Number : 1 Publisher : Nature Publishing Group . Antoine Toisoul, Jean Kossaifi, Adrian Bulat, Georgios Tzimiropoulos, and Maja Pantic. 2021. Estimation of continuous valence and arousal levels from faces in naturalistic conditions. Nature Machine Intelligence 3, 1 (Jan. 2021), 42--50. https:\/\/doi.org\/10.1038\/s42256-020-00280-0 Number: 1 Publisher: Nature Publishing Group."},{"key":"e_1_3_2_2_43_1","volume-title":"Modeling Dynamics of Facial Behavior for Mental Health Assessment. (Aug","author":"Tran Minh","year":"2021","unstructured":"Minh Tran , Ellen Bradley , Michelle Matvey , Joshua Woolley , and Mohammad Soleymani . 2021. Modeling Dynamics of Facial Behavior for Mental Health Assessment. (Aug . 2021 ). https:\/\/scirate.com\/arxiv\/2108.09934 Minh Tran, Ellen Bradley, Michelle Matvey, Joshua Woolley, and Mohammad Soleymani. 2021. Modeling Dynamics of Facial Behavior for Mental Health Assessment. (Aug. 2021). https:\/\/scirate.com\/arxiv\/2108.09934"},{"key":"e_1_3_2_2_44_1","volume-title":"International Symposium on Pervasive Computing Paradigms for Mental Health. Springer, 72--81","author":"Tron Talia","year":"2015","unstructured":"Talia Tron , Abraham Peled , Alexander Grinsphoon , and Daphna Weinshall . 2015 . Automated facial expressions analysis in schizophrenia: A continuous dynamic approach . In International Symposium on Pervasive Computing Paradigms for Mental Health. Springer, 72--81 . Talia Tron, Abraham Peled, Alexander Grinsphoon, and Daphna Weinshall. 2015. Automated facial expressions analysis in schizophrenia: A continuous dynamic approach. In International Symposium on Pervasive Computing Paradigms for Mental Health. Springer, 72--81."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/BHI.2016.7455874"},{"key":"e_1_3_2_2_46_1","volume-title":"arXiv:1706.03762 [cs] (Dec","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N. Gomez , Lukasz Kaiser , and Illia Polosukhin . 2017. Attention Is All You Need. arXiv:1706.03762 [cs] (Dec . 2017 ). http:\/\/arxiv.org\/abs\/1706.03762 arXiv: 1706.03762. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention Is All You Need. arXiv:1706.03762 [cs] (Dec. 2017). http:\/\/arxiv.org\/abs\/1706.03762 arXiv: 1706.03762."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6909"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00037"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2019.103649"},{"key":"e_1_3_2_2_50_1","volume-title":"Pose-based Body Language Recognition for Emotion and Psychiatric Symptom Interpretation. In 2020 25th International Conference on Pattern Recognition (ICPR). 294--301","author":"Yang Zhengyuan","year":"2021","unstructured":"Zhengyuan Yang , Amanda Kay , Yuncheng Li , Wendi Cross , and Jiebo Luo . 2021 . Pose-based Body Language Recognition for Emotion and Psychiatric Symptom Interpretation. In 2020 25th International Conference on Pattern Recognition (ICPR). 294--301 . https:\/\/doi.org\/10.1109\/ICPR48806.2021.9412591 Zhengyuan Yang, Amanda Kay, Yuncheng Li, Wendi Cross, and Jiebo Luo. 2021. Pose-based Body Language Recognition for Emotion and Psychiatric Symptom Interpretation. In 2020 25th International Conference on Pattern Recognition (ICPR). 294--301. https:\/\/doi.org\/10.1109\/ICPR48806.2021.9412591"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/FG47880.2020.00037"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00887"}],"event":{"name":"MM '22: The 30th ACM International Conference on Multimedia","location":"Lisboa Portugal","acronym":"MM '22","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 30th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3548373","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3503161.3548373","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:44Z","timestamp":1750186844000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3548373"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":52,"alternative-id":["10.1145\/3503161.3548373","10.1145\/3503161"],"URL":"https:\/\/doi.org\/10.1145\/3503161.3548373","relation":{},"subject":[],"published":{"date-parts":[[2022,10,10]]},"assertion":[{"value":"2022-10-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}