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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/2511.11244 |
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| _version_ | 1866909902298939392 |
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| author | Deng, Shijian Kosloski, Erin E. Vasireddy, Siva Sai Nagender Li, Jia Sherwood, Randi Sierra Hatha, Feroz Mohamed Patel, Siddhi Rollins, Pamela R Tian, Yapeng |
| author_facet | Deng, Shijian Kosloski, Erin E. Vasireddy, Siva Sai Nagender Li, Jia Sherwood, Randi Sierra Hatha, Feroz Mohamed Patel, Siddhi Rollins, Pamela R Tian, Yapeng |
| contents | The automatic detection of gaze targets in autistic children through artificial intelligence can be impactful, especially for those who lack access to a sufficient number of professionals to improve their quality of life. This paper introduces a new, real-world AI application for gaze target detection in autistic children, which predicts a child's point of gaze from an activity image. This task is foundational for building automated systems that can measure joint attention-a core challenge in Autism Spectrum Disorder (ASD). To facilitate the study of this challenging application, we collected the first-ever Autism Gaze Target (AGT) dataset. We further propose a novel Socially Aware Coarse-to-Fine (SACF) gaze detection framework that explicitly leverages the social context of a scene to overcome the class imbalance common in autism datasets-a consequence of autistic children's tendency to show reduced gaze to faces. It utilizes a two-pathway architecture with expert models specialized in social and non-social gaze, guided by a context-awareness gate module. The results of our comprehensive experiments demonstrate that our framework achieves new state-of-the-art performance for gaze target detection in this population, significantly outperforming existing methods, especially on the critical minority class of face-directed gaze. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11244 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Toward Gaze Target Detection of Young Autistic Children Deng, Shijian Kosloski, Erin E. Vasireddy, Siva Sai Nagender Li, Jia Sherwood, Randi Sierra Hatha, Feroz Mohamed Patel, Siddhi Rollins, Pamela R Tian, Yapeng Computer Vision and Pattern Recognition Artificial Intelligence The automatic detection of gaze targets in autistic children through artificial intelligence can be impactful, especially for those who lack access to a sufficient number of professionals to improve their quality of life. This paper introduces a new, real-world AI application for gaze target detection in autistic children, which predicts a child's point of gaze from an activity image. This task is foundational for building automated systems that can measure joint attention-a core challenge in Autism Spectrum Disorder (ASD). To facilitate the study of this challenging application, we collected the first-ever Autism Gaze Target (AGT) dataset. We further propose a novel Socially Aware Coarse-to-Fine (SACF) gaze detection framework that explicitly leverages the social context of a scene to overcome the class imbalance common in autism datasets-a consequence of autistic children's tendency to show reduced gaze to faces. It utilizes a two-pathway architecture with expert models specialized in social and non-social gaze, guided by a context-awareness gate module. The results of our comprehensive experiments demonstrate that our framework achieves new state-of-the-art performance for gaze target detection in this population, significantly outperforming existing methods, especially on the critical minority class of face-directed gaze. |
| title | Toward Gaze Target Detection of Young Autistic Children |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2511.11244 |