  Referring Expression Comprehension (REC) has become one of the most important
tasks in visual reasoning, since it is an essential step for many
vision-and-language tasks such as visual question answering. However, it has
not been widely used in many downstream tasks because it suffers 1) two-stage
methods exist heavy computation cost and inevitable error accumulation, and 2)
one-stage methods have to depend on lots of hyper-parameters (such as anchors)
to generate bounding box. In this paper, we present a proposal-free one-stage
(PFOS) model that is able to regress the region-of-interest from the image,
based on a textual query, in an end-to-end manner. Instead of using the
dominant anchor proposal fashion, we directly take the dense-grid of an image
as input for a cross-attention transformer that learns grid-word
correspondences. The final bounding box is predicted directly from the image
without the time-consuming anchor selection process that previous methods
suffer. Our model achieves the state-of-the-art performance on four referring
expression datasets with higher efficiency, comparing to previous best
one-stage and two-stage methods.
