Autism Spectrum Disorder (ASD) presents significant challenges in early
diagnosis and intervention, impacting children and their families. With
prevalence rates rising, there is a critical need for accessible and efficient
screening tools. Leveraging machine learning (ML) techniques, in particular
Temporal Action Localization (TAL), holds promise for automating ASD screening.
This paper introduces a self-attention based TAL model designed to identify
ASD-related behaviors in infant videos. Unlike existing methods, our approach
simplifies complex modeling and emphasizes efficiency, which is essential for
practical deployment in real-world scenarios. Importantly, this work
underscores the importance of developing computer vision methods capable of
operating in naturilistic environments with little equipment control,
addressing key challenges in ASD screening. This study is the first to conduct
end-to-end temporal action localization in untrimmed videos of infants with
ASD, offering promising avenues for early intervention and support. We report
baseline results of behavior detection using our TAL model. We achieve 70%
accuracy for look face, 79% accuracy for look object, 72% for smile and 65% for
vocalization.

采用机器学习技术和自注意力的时间动作定位模型，针对自闭症谱系障碍（ASD）的幼儿视频，发展了简化复杂建模和强调效率的 ASD 行为识别方法，为 ASD 筛查提供了有前景的早期干预和支持方案，成为首个实施未剪辑的 ASD 幼儿视频中端到端时态行动定位的研究。