Autism Spectrum Disorder (ASD) can affect individuals at varying degrees of intensity, from challenges in overall health, communication, and sensory processing, and this often begins at a young age. Thus, it is critical for medical professionals to be able to accurately diagnose ASD in young children, but doing so is difficult. Deep learning can be responsibly leveraged to improve productivity in addressing this task. The availability of data, however, remains a considerable obstacle. Hence, in this work, we introduce the Video ASD dataset--a dataset that contains video frame convolutional and attention map feature data--to foster further progress in the task of ASD classification. The original videos showcase children reacting to chemo-sensory stimuli, among auditory, touch, and vision This dataset contains the features of the frames spanning 2,467 videos, for a total of approximately 1.4 million frames. Additionally, head pose angles are included to account for head movement noise, as well as full-sentence text labels for the taste and smell videos that describe how the facial expression changes before, immediately after, and long after interaction with the stimuli. In addition to providing features, we also test foundation models on this data to showcase how movement noise affects performance and the need for more data and more complex labels.

本研究解决了准确诊断自闭症谱系障碍（ASD）在年轻儿童中的难题，特别是在数据可用性方面的不足。提出了视频ASD数据集，包含2,467个视频的特征数据，展示儿童对化学感官刺激的反应，旨在促进ASD分类的进展。研究发现，运动噪声对模型性能的影响凸显了复杂标签和更多数据需求的重要性。

基于额外刺激行为的新型自闭症分类视频数据集