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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.05299 |
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| _version_ | 1866908481072660480 |
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| author | Wu, Meiqi Kang, Yaxuan Li, Xuchen Hu, Shiyu Chen, Xiaotang Kang, Yunfeng Wang, Weiqiang Huang, Kaiqi |
| author_facet | Wu, Meiqi Kang, Yaxuan Li, Xuchen Hu, Shiyu Chen, Xiaotang Kang, Yunfeng Wang, Weiqiang Huang, Kaiqi |
| contents | The Drawing Projection Test (DPT) is an essential tool in art therapy, allowing psychologists to assess participants' mental states through their sketches. Specifically, through sketches with the theme of "a person picking an apple from a tree (PPAT)", it can be revealed whether the participants are in mental states such as depression. Compared with scales, the DPT can enrich psychologists' understanding of an individual's mental state. However, the interpretation of the PPAT is laborious and depends on the experience of the psychologists. To address this issue, we propose an effective identification method to support psychologists in conducting a large-scale automatic DPT. Unlike traditional sketch recognition, DPT more focus on the overall evaluation of the sketches, such as color usage and space utilization. Moreover, PPAT imposes a time limit and prohibits verbal reminders, resulting in low drawing accuracy and a lack of detailed depiction. To address these challenges, we propose the following efforts: (1) Providing an experimental environment for automated analysis of PPAT sketches for depression assessment; (2) Offering a Visual-Semantic depression assessment based on LLM (VS-LLM) method; (3) Experimental results demonstrate that our method improves by 17.6% compared to the psychologist assessment method. We anticipate that this work will contribute to the research in mental state assessment based on PPAT sketches' elements recognition. Our datasets and codes are available at https://github.com/wmeiqi/VS-LLM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05299 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VS-LLM: Visual-Semantic Depression Assessment based on LLM for Drawing Projection Test Wu, Meiqi Kang, Yaxuan Li, Xuchen Hu, Shiyu Chen, Xiaotang Kang, Yunfeng Wang, Weiqiang Huang, Kaiqi Computer Vision and Pattern Recognition Artificial Intelligence The Drawing Projection Test (DPT) is an essential tool in art therapy, allowing psychologists to assess participants' mental states through their sketches. Specifically, through sketches with the theme of "a person picking an apple from a tree (PPAT)", it can be revealed whether the participants are in mental states such as depression. Compared with scales, the DPT can enrich psychologists' understanding of an individual's mental state. However, the interpretation of the PPAT is laborious and depends on the experience of the psychologists. To address this issue, we propose an effective identification method to support psychologists in conducting a large-scale automatic DPT. Unlike traditional sketch recognition, DPT more focus on the overall evaluation of the sketches, such as color usage and space utilization. Moreover, PPAT imposes a time limit and prohibits verbal reminders, resulting in low drawing accuracy and a lack of detailed depiction. To address these challenges, we propose the following efforts: (1) Providing an experimental environment for automated analysis of PPAT sketches for depression assessment; (2) Offering a Visual-Semantic depression assessment based on LLM (VS-LLM) method; (3) Experimental results demonstrate that our method improves by 17.6% compared to the psychologist assessment method. We anticipate that this work will contribute to the research in mental state assessment based on PPAT sketches' elements recognition. Our datasets and codes are available at https://github.com/wmeiqi/VS-LLM. |
| title | VS-LLM: Visual-Semantic Depression Assessment based on LLM for Drawing Projection Test |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2508.05299 |