  Diagnostic and intervention methodologies for skill assessment of autism
typically requires a clinician repetitively initiating several stimuli and
recording the child's response. In this paper, we propose to automate the
response measurement through video recording of the scene following the use of
Deep Neural models for human action recognition from videos. However,
supervised learning of neural networks demand large amounts of annotated data
that are hard to come by. This issue is addressed by leveraging the
`similarities' between the action categories in publicly available large-scale
video action (source) datasets and the dataset of interest. A technique called
guided weak supervision is proposed, where every class in the target data is
matched to a class in the source data using the principle of posterior
likelihood maximization. Subsequently, classifier on the target data is
re-trained by augmenting samples from the matched source classes, along with a
new loss encouraging inter-class separability. The proposed method is evaluated
on two skill assessment autism datasets, SSBD and a real world Autism dataset
comprising 37 children of different ages and ethnicity who are diagnosed with
autism. Our proposed method is found to improve the performance of the
state-of-the-art multi-class human action recognition models in-spite of
supervision with scarce data.
