AI-Augmented Art Psychotherapy through a Hierarchical Co-Attention Mechanism
- Authors
- Jin, Seungwan; Choi, Hoyoung; Han, Kyungsik
- Issue Date
- Oct-2022
- Publisher
- ACM
- Keywords
- hierarchical co-attention; human-centered ai; multimodal learning
- Citation
- ACM Conference on Information and Knowledge Management, pp.4089 - 4093
- Indexed
- OTHER
- Journal Title
- ACM Conference on Information and Knowledge Management
- Start Page
- 4089
- End Page
- 4093
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/188730
- DOI
- 10.1145/3511808.3557542
- 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.
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