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AI-Augmented Art Psychotherapy through a Hierarchical Co-Attention Mechanism

Published: 17 October 2022 Publication History

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.

Supplementary Material

MP4 File (CIKM22-sp0116.mp4)
A study on developing an AI model to support art psychotherapists' decision-making in the diagnostic process of projective drawing test.

References

[1]
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. 2015. Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision. 2425--2433.
[2]
Stephanie L Brooke. 2004. Tools of the trade: A therapist's guide to art therapy assessments. Charles C Thomas Publisher.
[3]
DV Ciechetti and SS Sparrow. 1981. Developing criteria for establishing inter-rater reliability of specific items in a given inventory: application of the assessment of adaptive behavior. American Journal of Mental Deficiency 86 (1981), 127--137.
[4]
Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning. 2020. Electra: Pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555 (2020).
[5]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
[6]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778.
[7]
Wonjae Kim, Bokyung Son, and Ildoo Kim. 2021. Vilt: Vision-and-language transformer without convolution or region supervision. In International Conference on Machine Learning. PMLR, 5583--5594.
[8]
Heidi S Lack. 1997. The person-in-the-rain projective drawing as a measure of children's coping capacity: a concurrent validity study using rorschach, psychiatric, and life history variables. (1997).
[9]
Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. 2021. Align before fuse: Vision and language representation learning with momentum distillation. Advances in Neural Information Processing Systems 34 (2021).
[10]
Jiasen Lu, Jianwei Yang, Dhruv Batra, and Devi Parikh. 2016. Hierarchical question-image co-attention for visual question answering. Advances in neural information processing systems 29 (2016).
[11]
Karen Machover. 1949. Personality projection in the drawing of the human figure: A method of personality investigation. (1949).
[12]
Donald C Mattson. 2009. Accessible image analysis for art assessment. The Arts in Psychotherapy 36, 4 (2009), 208--213.
[13]
Karen Simonyan and AndrewZisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).
[14]
Mingxing Tan and Quoc Le. 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning. PMLR, 6105--6114.
[15]
Glyn V Thomas and Angele MJ Silk. 1990. An introduction to the psychology of children's drawings. New York University Press.
[16]
Lisa R Willis, Stephen P Joy, and Donna H Kaiser. 2010. Draw-a-Person-in-the-Rain as an assessment of stress and coping resources. The Arts in Psychotherapy 37, 3 (2010), 233--239.

Cited By

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  • (2024)AI-Generated Art in Art Therapy: Insights from Art Therapists Using a Mixed Methods Approach (Preprint)JMIR Formative Research10.2196/63038Online publication date: 7-Jun-2024
  • (2024)SceneDAPR: A Scene-Level Free-Hand Drawing Dataset for Web-based Psychological Drawing AssessmentProceedings of the ACM Web Conference 202410.1145/3589334.3648150(4630-4641)Online publication date: 13-May-2024
  • (2023)A Study on User Perception and Experience Differences in Recommendation Results by Domain Expertise: The Case of Fashion DomainsExtended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544549.3585641(1-7)Online publication date: 19-Apr-2023

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  1. AI-Augmented Art Psychotherapy through a Hierarchical Co-Attention Mechanism

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        cover image ACM Conferences
        CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management
        October 2022
        5274 pages
        ISBN:9781450392365
        DOI:10.1145/3511808
        • General Chairs:
        • Mohammad Al Hasan,
        • Li Xiong
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Published: 17 October 2022

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        Author Tags

        1. hierarchical co-attention
        2. human-centered ai
        3. multimodal learning

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        • The Institute of Information & communications Technology Planning & Evaluation
        • The National Research Foundation

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        CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;
        Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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        Cited By

        View all
        • (2024)AI-Generated Art in Art Therapy: Insights from Art Therapists Using a Mixed Methods Approach (Preprint)JMIR Formative Research10.2196/63038Online publication date: 7-Jun-2024
        • (2024)SceneDAPR: A Scene-Level Free-Hand Drawing Dataset for Web-based Psychological Drawing AssessmentProceedings of the ACM Web Conference 202410.1145/3589334.3648150(4630-4641)Online publication date: 13-May-2024
        • (2023)A Study on User Perception and Experience Differences in Recommendation Results by Domain Expertise: The Case of Fashion DomainsExtended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544549.3585641(1-7)Online publication date: 19-Apr-2023

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