  In this work, we address the task of referring image segmentation (RIS),
which aims at predicting a segmentation mask for the object described by a
natural language expression. Most existing methods focus on establishing
unidirectional or directional relationships between visual and linguistic
features to associate two modalities together, while the multi-scale context is
ignored or insufficiently modeled. Multi-scale context is crucial to localize
and segment those objects that have large scale variations during the
multi-modal fusion process. To solve this problem, we propose a simple yet
effective Cascaded Multi-modal Fusion (CMF) module, which stacks multiple
atrous convolutional layers in parallel and further introduces a cascaded
branch to fuse visual and linguistic features. The cascaded branch can
progressively integrate multi-scale contextual information and facilitate the
alignment of two modalities during the multi-modal fusion process. Experimental
results on four benchmark datasets demonstrate that our method outperforms most
state-of-the-art methods. Code is available at
https://github.com/jianhua2022/CMF-Refseg.
