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.

本文提出了一种跨模态自注意模块 (CMSA)，可以有效地捕捉语言和视觉特征之间的长距离依赖关系，并且采用门控多层融合模块，以选择性地集成不同级别的特征。在多个数据集上的验证表明，我们的方法在图像分割任务上显著优于现有的最先进的方法。