  Referring Image Segmentation (RIS) is a challenging task that requires an
algorithm to segment objects referred by free-form language expressions.
Despite significant progress in recent years, most state-of-the-art (SOTA)
methods still suffer from considerable language-image modality gap at the pixel
and word level. These methods generally 1) rely on sentence-level language
features for language-image alignment and 2) lack explicit training supervision
for fine-grained visual grounding. Consequently, they exhibit weak object-level
correspondence between visual and language features. Without well-grounded
features, prior methods struggle to understand complex expressions that require
strong reasoning over relationships among multiple objects, especially when
dealing with rarely used or ambiguous clauses. To tackle this challenge, we
introduce a novel Mask Grounding auxiliary task that significantly improves
visual grounding within language features, by explicitly teaching the model to
learn fine-grained correspondence between masked textual tokens and their
matching visual objects. Mask Grounding can be directly used on prior RIS
methods and consistently bring improvements. Furthermore, to holistically
address the modality gap, we also design a cross-modal alignment loss and an
accompanying alignment module. These additions work synergistically with Mask
Grounding. With all these techniques, our comprehensive approach culminates in
MagNet (Mask-grounded Network), an architecture that significantly outperforms
prior arts on three key benchmarks (RefCOCO, RefCOCO+ and G-Ref), demonstrating
our method's effectiveness in addressing current limitations of RIS algorithms.
Our code and pre-trained weights will be released.
