Toward Gaze Target Detection of Young Autistic Children
Shijian Deng, Erin E. Kosloski, Siva Sai Nagender Vasireddy, Jia Li, Randi Sierra Sherwood, Feroz Mohamed Hatha, Siddhi Patel, Pamela R Rollins, Yapeng Tian

TL;DR
This paper presents a novel AI framework and dataset for detecting gaze targets in autistic children, leveraging social context to improve accuracy and address class imbalance, advancing automated assessment of joint attention in ASD.
Contribution
Introduces the first Autism Gaze Target dataset and a socially aware coarse-to-fine framework that significantly improves gaze detection accuracy in autistic children.
Findings
Achieves state-of-the-art performance on gaze detection in autistic children.
Effectively handles class imbalance by leveraging social context.
Outperforms existing methods, especially on face-directed gaze detection.
Abstract
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…
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Taxonomy
TopicsGaze Tracking and Assistive Technology · Autism Spectrum Disorder Research · Visual Attention and Saliency Detection
