Weakly-supervised Autism Severity Assessment in Long Videos
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911953565253632 |
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| author | Ali, Abid Ali, Mahmoud Odobez, Jean-Marc Barbini, Camilla Dubuisson, Séverine Bremond, Francois Thümmler, Susanne |
| author_facet | Ali, Abid Ali, Mahmoud Odobez, Jean-Marc Barbini, Camilla Dubuisson, Séverine Bremond, Francois Thümmler, Susanne |
| contents | Autism Spectrum Disorder (ASD) is a diverse collection of neurobiological conditions marked by challenges in social communication and reciprocal interactions, as well as repetitive and stereotypical behaviors. Atypical behavior patterns in a long, untrimmed video can serve as biomarkers for children with ASD. In this paper, we propose a video-based weakly-supervised method that takes spatio-temporal features of long videos to learn typical and atypical behaviors for autism detection. On top of that, we propose a shallow TCN-MLP network, which is designed to further categorize the severity score. We evaluate our method on actual evaluation videos of children with autism collected and annotated (for severity score) by clinical professionals. Experimental results demonstrate the effectiveness of behavioral biomarkers that could help clinicians in autism spectrum analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_09159 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Weakly-supervised Autism Severity Assessment in Long Videos Ali, Abid Ali, Mahmoud Odobez, Jean-Marc Barbini, Camilla Dubuisson, Séverine Bremond, Francois Thümmler, Susanne Computer Vision and Pattern Recognition Autism Spectrum Disorder (ASD) is a diverse collection of neurobiological conditions marked by challenges in social communication and reciprocal interactions, as well as repetitive and stereotypical behaviors. Atypical behavior patterns in a long, untrimmed video can serve as biomarkers for children with ASD. In this paper, we propose a video-based weakly-supervised method that takes spatio-temporal features of long videos to learn typical and atypical behaviors for autism detection. On top of that, we propose a shallow TCN-MLP network, which is designed to further categorize the severity score. We evaluate our method on actual evaluation videos of children with autism collected and annotated (for severity score) by clinical professionals. Experimental results demonstrate the effectiveness of behavioral biomarkers that could help clinicians in autism spectrum analysis. |
| title | Weakly-supervised Autism Severity Assessment in Long Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.09159 |