Text-to-image retrieval is an essential task in cross-modal information
retrieval, i.e., retrieving relevant images from a large and unlabelled dataset
given textual queries. In this paper, we propose VisualSparta, a novel
(Visual-text Sparse Transformer Matching) model that shows significant
improvement in terms of both accuracy and efficiency. VisualSparta is capable
of outperforming previous state-of-the-art scalable methods in MSCOCO and
Flickr30K. We also show that it achieves substantial retrieving speed
advantages, i.e., for a 1 million image index, VisualSparta using CPU gets
~391X speedup compared to CPU vector search and ~5.4X speedup compared to
vector search with GPU acceleration. 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 large-scale datasets, with significant accuracy
improvement compared to previous state-of-the-art methods.

本文介绍了一种名为 VisualSparta 的基于稀疏变换匹配的 Transformer 模型，旨在实现跨模态信息检索领域中的文本检索图像任务，具有较高准确性和效率，可在大规模数据集中实现实时搜索。