  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.
