  Referring Image Segmentation (RIS) is a cross-modal task that aims to segment
an instance described by a natural language expression. Recent methods leverage
large-scale pretrained unimodal models as backbones along with fusion
techniques for joint reasoning across modalities. However, the inherent
cross-modal nature of RIS raises questions about the effectiveness of unimodal
backbones. We propose RISCLIP, a novel framework that effectively leverages the
cross-modal nature of CLIP for RIS. Observing CLIP's inherent alignment between
image and text features, we capitalize on this starting point and introduce
simple but strong modules that enhance unimodal feature extraction and leverage
rich alignment knowledge in CLIP's image-text shared-embedding space. RISCLIP
exhibits outstanding results on all three major RIS benchmarks and also
outperforms previous CLIP-based methods, demonstrating the efficacy of our
strategy in extending CLIP's image-text alignment to RIS.
