Towards Generalizable Referring Image Segmentation Via Target Prompt And Visual Coherence

Referring image segmentation (RIS) aims to segment objects in an image conditioning on free-form text descriptions. Despite the overwhelming progress, it still remains challenging for current approaches to perform well on cases with various text expressions or with unseen visual entities, limiting its further application. In this paper, we present a novel RIS approach, which substantially improves the generalization ability by addressing the two dilemmas mentioned above. Specially, to deal with unconstrained texts, we propose to boost a given expression with an explicit and crucial prompt, which complements the expression in a unified context, facilitating target capturing in the presence of linguistic style changes. Furthermore, we introduce a multi-modal fusion aggregation module with visual guidance from a powerful pretrained model to leverage spatial relations and pixel coherences to handle the incomplete target masks and false positive irregular clumps which often appear on unseen visual entities. Extensive experiments are conducted in the zero-shot cross-dataset settings and the proposed approach achieves consistent gains compared to the state-of-the-art, e.g., $4.15 \%$, $5.45 \%$, and $4.64 \%$ mIoU increase on RefCOCO, RefCOCO+ and ReferIt respectively, demonstrating its effectiveness.