  We consider the problem of referring image segmentation. Given an input image
and a natural language expression, the goal is to segment the object referred
by the language expression in the image. Existing works in this area treat the
language expression and the input image separately in their representations.
They do not sufficiently capture long-range correlations between these two
modalities. In this paper, we propose a cross-modal self-attention (CMSA)
module that effectively captures the long-range dependencies between linguistic
and visual features. Our model can adaptively focus on informative words in the
referring expression and important regions in the input image. In addition, we
propose a gated multi-level fusion module to selectively integrate
self-attentive cross-modal features corresponding to different levels in the
image. This module controls the information flow of features at different
levels. We validate the proposed approach on four evaluation datasets. Our
proposed approach consistently outperforms existing state-of-the-art methods.
