{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:02:03Z","timestamp":1750309323885,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":18,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T00:00:00Z","timestamp":1722556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,2]]},"DOI":"10.1145\/3696271.3696281","type":"proceedings-article","created":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T10:47:56Z","timestamp":1733136476000},"page":"59-65","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Deep Learning-based Depression Level Estimation Based on Multi-task Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7253-5278","authenticated-orcid":false,"given":"Qingxin","family":"Ye","sequence":"first","affiliation":[{"name":"Provincial key Laboratory of Computational Science, Huaqiao University, Quanzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2130-2153","authenticated-orcid":false,"given":"Zhenming","family":"Xie","sequence":"additional","affiliation":[{"name":"Provincial key Laboratory of Computational Science, Huaqiao University, Quanzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8094-1991","authenticated-orcid":false,"given":"Hao","family":"Sun","sequence":"additional","affiliation":[{"name":"Intelligent Image Processing Lab, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8431-673X","authenticated-orcid":false,"given":"Luyao","family":"Xin","sequence":"additional","affiliation":[{"name":"Provincial key Laboratory of Computational Science, Huaqiao University, Quanzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9904-604X","authenticated-orcid":false,"given":"Youwen","family":"Chen","sequence":"additional","affiliation":[{"name":"Provincial key Laboratory of Computational Science, Huaqiao University, Quanzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2998-7691","authenticated-orcid":false,"given":"Jian","family":"Song","sequence":"additional","affiliation":[{"name":"Provincial key Laboratory of Computational Science, Huaqiao University, Quanzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5952-0188","authenticated-orcid":false,"given":"Yen-Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"Intelligent Image Processing Lab, Ritsumeikan University, Osaka, Japan"}]}],"member":"320","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"key":"e_1_3_3_1_1_2","unstructured":"World Health Organization. Depressive disorder (depression)[R]. (2023-3-31) [2024-4-10]. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/depression."},{"key":"e_1_3_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Ringeval F Schuller B Valstar M et al. AVEC 2019 workshop and challenge: state-of-mind detecting depression with AI and cross-cultural affect recognition[C]\/\/Proceedings of the 9th International on Audio\/visual Emotion Challenge and Workshop. 2019: 3-12.","DOI":"10.1145\/3347320.3357688"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21144764"},{"volume-title":"multi-modal measurement of depression using deep learning models[C]\/\/Proceedings of the 7th annual workshop on audio\/visual emotion challenge. 2017: 53-59","author":"Yang L","key":"e_1_3_3_1_4_2","unstructured":"Yang L, Jiang D, Xia X, et al. multi-modal measurement of depression using deep learning models[C]\/\/Proceedings of the 7th annual workshop on audio\/visual emotion challenge. 2017: 53-59."},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.27012\/d.cnki.gdhuu.2023.000688"},{"key":"e_1_3_3_1_6_2","volume-title":"Auxiliary tasks in multi-task learning[J]. arXiv preprint arXiv:1805.06334","author":"Liebel L","year":"2018","unstructured":"Liebel L, K\u00f6rner M. Auxiliary tasks in multi-task learning[J]. arXiv preprint arXiv:1805.06334, 2018."},{"key":"e_1_3_3_1_7_2","volume-title":"Identifying depressive symptoms from tweets: Figurative language enabled multi-task learning framework[J]. arXiv preprint arXiv:2011.06149","author":"Yadav S","year":"2020","unstructured":"Yadav S, Chauhan J, Sain J P, et al. Identifying depressive symptoms from tweets: Figurative language enabled multi-task learning framework[J]. arXiv preprint arXiv:2011.06149, 2020."},{"key":"e_1_3_3_1_8_2","volume-title":"Multi-Modal and Multi-Task Depression Detection with Sentiment Assistance[C]\/\/2024 IEEE International Conference on Consumer Electronics (ICCE)","author":"Teng S","year":"2024","unstructured":"Teng S, Chai S, Liu J, et al. Multi-Modal and Multi-Task Depression Detection with Sentiment Assistance[C]\/\/2024 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2024: 1-5."},{"key":"e_1_3_3_1_9_2","volume-title":"WORCESTER POLYTECHNIC INSTITUTE","author":"Shrestha A.","year":"2024","unstructured":"Shrestha A. Multi-Task Learning to Screen for Major Depressive Disorder and Post-Traumatic Stress Disorder[D]. WORCESTER POLYTECHNIC INSTITUTE, 2024."},{"key":"e_1_3_3_1_10_2","first-page":"255","article-title":"A concordance correlation coefficient to evaluate reproducibility","volume":"1989","author":"Lawrence I.","unstructured":"Lawrence, I.; Lin, K. A concordance correlation coefficient to evaluate reproducibility. Biometrics 1989, 45, 255-268.","journal-title":"Biometrics"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"crossref","unstructured":"Ringeval F Schuller B Valstar M et al. AVEC 2019 workshop and challenge: state-of-mind detecting depression with AI and cross-cultural affect recognition[C]\/\/Proceedings of the 9th International on Audio\/visual Emotion Challenge and Workshop. 2019: 3-12.","DOI":"10.1145\/3347320.3357688"},{"key":"e_1_3_3_1_12_2","volume-title":"The PHQ-8 as a measure of current depression in the general population[J]. Journal of affective disorders","author":"Kroenke K","year":"2009","unstructured":"Kroenke K, Strine T W, Spitzer R L, et al. The PHQ-8 as a measure of current depression in the general population[J]. Journal of affective disorders, 2009, 114(1-3): 163-173."},{"key":"e_1_3_3_1_13_2","unstructured":"Gratch J Artstein R Lucas G M et al. The distress analysis interview corpus of human and computer interviews[C]\/\/LREC. 2014: 3123-3128."},{"volume-title":"multi-modal fusion of Bert-CNN and gated CNN representations for depression detection[C]\/\/Proceedings of the 9th International on Audio\/Visual Emotion Challenge and Workshop. 2019: 55-63","author":"Rodrigues Makiuchi M","key":"e_1_3_3_1_14_2","unstructured":"Rodrigues Makiuchi M, Warnita T, Uto K, et al. multi-modal fusion of Bert-CNN and gated CNN representations for depression detection[C]\/\/Proceedings of the 9th International on Audio\/Visual Emotion Challenge and Workshop. 2019: 55-63."},{"key":"e_1_3_3_1_15_2","volume-title":"Validation and calibration of the patient health questionnaire (PHQ-9) in Argentina[J]. BMC psychiatry","author":"Urtasun M","year":"2019","unstructured":"Urtasun M, Daray F M, Teti G L, et al. Validation and calibration of the patient health questionnaire (PHQ-9) in Argentina[J]. BMC psychiatry, 2019, 19: 1-10."},{"key":"e_1_3_3_1_16_2","volume-title":"Attention is all you need[J]. Advances in neural information processing systems","author":"Vaswani A","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30."},{"key":"e_1_3_3_1_17_2","volume-title":"Pytorch: An imperative style, high-performance deep learning library[J]. Advances in neural information processing systems","author":"Paszke A","year":"2019","unstructured":"Paszke A, Gross S, Massa F, et al. Pytorch: An imperative style, high-performance deep learning library[J]. Advances in neural information processing systems, 2019, 32."},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2003.10.042"}],"event":{"name":"MLMI 2024: 2024 The 7th International Conference on Machine Learning and Machine Intelligence (MLMI)","acronym":"MLMI 2024","location":"Osaka Japan"},"container-title":["Proceedings of the 2024 7th International Conference on Machine Learning and Machine Intelligence (MLMI)"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696271.3696281","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3696271.3696281","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:00Z","timestamp":1750291440000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696271.3696281"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,2]]},"references-count":18,"alternative-id":["10.1145\/3696271.3696281","10.1145\/3696271"],"URL":"https:\/\/doi.org\/10.1145\/3696271.3696281","relation":{},"subject":[],"published":{"date-parts":[[2024,8,2]]},"assertion":[{"value":"2024-12-02","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}