  Computer Vision (CV) classifiers which distinguish and detect nonverbal
social human behavior and mental state can aid digital diagnostics and
therapeutics for psychiatry and the behavioral sciences. While CV classifiers
for traditional and structured classification tasks can be developed with
standard machine learning pipelines for supervised learning consisting of data
labeling, preprocessing, and training a convolutional neural network, there are
several pain points which arise when attempting this process for behavioral
phenotyping. Here, we discuss the challenges and corresponding opportunities in
this space, including handling heterogeneous data, avoiding biased models,
labeling massive and repetitive data sets, working with ambiguous or compound
class labels, managing privacy concerns, creating appropriate representations,
and personalizing models. We discuss current state-of-the-art research
endeavors in CV such as data curation, data augmentation, crowdsourced
labeling, active learning, reinforcement learning, generative models,
representation learning, federated learning, and meta-learning. We highlight at
least some of the machine learning advancements needed for imaging classifiers
to detect human social cues successfully and reliably.
