  We present a fully automated framework for building object detectors on
satellite imagery without requiring any human annotation or intervention. We
achieve this by leveraging the combined power of modern generative models
(e.g., StyleGAN) and recent advances in multi-modal learning (e.g., CLIP).
While deep generative models effectively encode the key semantics pertinent to
a data distribution, this information is not immediately accessible for
downstream tasks, such as object detection. In this work, we exploit CLIP's
ability to associate image features with text descriptions to identify neurons
in the generator network, which are subsequently used to build detectors
on-the-fly.
