  We present a new task of query auto-completion for estimating instance
probabilities. We complete a user query prefix conditioned upon an image. Given
the complete query, we fine tune a BERT embedding for estimating probabilities
of a broad set of instances. The resulting instance probabilities are used for
selection while being agnostic to the segmentation or attention mechanism. Our
results demonstrate that auto-completion using both language and vision
performs better than using only language, and that fine tuning a BERT embedding
allows to efficiently rank instances in the image. In the spirit of
reproducible research we make our data, models, and code available.
