  From content moderation to wildlife conservation, the number of applications
that require models to recognize nuanced or subjective visual concepts is
growing. Traditionally, developing classifiers for such concepts requires
substantial manual effort measured in hours, days, or even months to identify
and annotate data needed for training. Even with recently proposed Agile
Modeling techniques, which enable rapid bootstrapping of image classifiers,
users are still required to spend 30 minutes or more of monotonous, repetitive
data labeling just to train a single classifier. Drawing on Fiske's Cognitive
Miser theory, we propose a new framework that alleviates manual effort by
replacing human labeling with natural language interactions, reducing the total
effort required to define a concept by an order of magnitude: from labeling
2,000 images to only 100 plus some natural language interactions. Our framework
leverages recent advances in foundation models, both large language models and
vision-language models, to carve out the concept space through conversation and
by automatically labeling training data points. Most importantly, our framework
eliminates the need for crowd-sourced annotations. Moreover, our framework
ultimately produces lightweight classification models that are deployable in
cost-sensitive scenarios. Across 15 subjective concepts and across 2 public
image classification datasets, our trained models outperform traditional Agile
Modeling as well as state-of-the-art zero-shot classification models like
ALIGN, CLIP, CuPL, and large visual question-answering models like PaLI-X.
