  Text-to-image retrieval is an essential task in multi-modal information
retrieval, i.e. retrieving relevant images from a large and unlabelled image
dataset given textual queries. In this paper, we propose VisualSparta, a novel
text-to-image retrieval model that shows substantial improvement over existing
models on both accuracy and efficiency. We show that VisualSparta is capable of
outperforming all previous scalable methods in MSCOCO and Flickr30K. It also
shows substantial retrieving speed advantages, i.e. for an index with 1 million
images, VisualSparta gets over 391x speed up compared to standard vector
search. Experiments show that this speed advantage even gets bigger for larger
datasets because VisualSparta can be efficiently implemented as an inverted
index. To the best of our knowledge, VisualSparta is the first
transformer-based text-to-image retrieval model that can achieve real-time
searching for very large dataset, with significant accuracy improvement
compared to previous state-of-the-art methods.
