  Referring image segmentation aims at segmenting the foreground masks of the
entities that can well match the description given in the natural language
expression. Previous approaches tackle this problem using implicit feature
interaction and fusion between visual and linguistic modalities, but usually
fail to explore informative words of the expression to well align features from
the two modalities for accurately identifying the referred entity. In this
paper, we propose a Cross-Modal Progressive Comprehension (CMPC) module and a
Text-Guided Feature Exchange (TGFE) module to effectively address the
challenging task. Concretely, the CMPC module first employs entity and
attribute words to perceive all the related entities that might be considered
by the expression. Then, the relational words are adopted to highlight the
correct entity as well as suppress other irrelevant ones by multimodal graph
reasoning. In addition to the CMPC module, we further leverage a simple yet
effective TGFE module to integrate the reasoned multimodal features from
different levels with the guidance of textual information. In this way,
features from multi-levels could communicate with each other and be refined
based on the textual context. We conduct extensive experiments on four popular
referring segmentation benchmarks and achieve new state-of-the-art
performances.
