AI-Augmented Art Psychotherapy through a Hierarchical Co-Attention Mechanism
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jin, Seungwan | - |
dc.contributor.author | Choi, Hoyoung | - |
dc.contributor.author | Han, Kyungsik | - |
dc.date.accessioned | 2023-08-01T07:14:49Z | - |
dc.date.available | 2023-08-01T07:14:49Z | - |
dc.date.created | 2023-07-20 | - |
dc.date.issued | 2022-10 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/188730 | - |
dc.description.abstract | One of the significant social problems emerging in modern society is mental illness, and a growing number of people are seeking psychological help. Art therapy is a technique that can alleviate psychological and emotional conflicts through creation. However, the expression of a drawing varies by individuals, and the subjective judgments made by art therapists raise the need to secure an objective assessment. In this paper, we present M2C (Multimodal classification with 2-stage Co-attention), a deep learning model that predicts stress from art therapy psychological test data. M2C employs a co-attention mechanism that combines two modalities-drawings and post-questionnaire answers-to complement the weaknesses of each, which corresponds to therapists' psychometric diagnostic processes. The results of the experiment show that M2C yielded higher performance than other state-of-the-art single- or multi-modal models, demonstrating the effectiveness of the co-attention approach that reflects the diagnosis process. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ACM | - |
dc.title | AI-Augmented Art Psychotherapy through a Hierarchical Co-Attention Mechanism | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Han, Kyungsik | - |
dc.identifier.doi | 10.1145/3511808.3557542 | - |
dc.identifier.scopusid | 2-s2.0-85140823935 | - |
dc.identifier.wosid | 001074639604024 | - |
dc.identifier.bibliographicCitation | ACM Conference on Information and Knowledge Management, pp.4089 - 4093 | - |
dc.relation.isPartOf | ACM Conference on Information and Knowledge Management | - |
dc.citation.title | ACM Conference on Information and Knowledge Management | - |
dc.citation.startPage | 4089 | - |
dc.citation.endPage | 4093 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | other | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.subject.keywordPlus | Attention mechanisms | - |
dc.subject.keywordPlus | Hierarchical co-attention | - |
dc.subject.keywordPlus | Human-centered ai | - |
dc.subject.keywordPlus | Mental illness | - |
dc.subject.keywordPlus | Multi-modal | - |
dc.subject.keywordPlus | Multi-modal learning | - |
dc.subject.keywordPlus | Number of peoples | - |
dc.subject.keywordPlus | Objective assessment | - |
dc.subject.keywordPlus | Social problems | - |
dc.subject.keywordPlus | Subjective judgement | - |
dc.subject.keywordAuthor | hierarchical co-attention | - |
dc.subject.keywordAuthor | human-centered ai | - |
dc.subject.keywordAuthor | multimodal learning | - |
dc.identifier.url | https://doi.org/10.1145/3511808.3557542 | - |
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