  Referring image segmentation aims to segment the objects referred by a
natural language expression. Previous methods usually focus on designing an
implicit and recurrent feature interaction mechanism to fuse the
visual-linguistic features to directly generate the final segmentation mask
without explicitly modeling the localization information of the referent
instances. To tackle these problems, we view this task from another perspective
by decoupling it into a "Locate-Then-Segment" (LTS) scheme. Given a language
expression, people generally first perform attention to the corresponding
target image regions, then generate a fine segmentation mask about the object
based on its context. The LTS first extracts and fuses both visual and textual
features to get a cross-modal representation, then applies a cross-model
interaction on the visual-textual features to locate the referred object with
position prior, and finally generates the segmentation result with a
light-weight segmentation network. Our LTS is simple but surprisingly
effective. On three popular benchmark datasets, the LTS outperforms all the
previous state-of-the-art methods by a large margin (e.g., +3.2% on RefCOCO+
and +3.4% on RefCOCOg). In addition, our model is more interpretable with
explicitly locating the object, which is also proved by visualization
experiments. We believe this framework is promising to serve as a strong
baseline for referring image segmentation.
