Cross-Attentive Multimodal Enhancement for Weakly-Supervised Referring Image Semantic Segmentation

Weakly Supervised Semantic Segmentation(WSSS) reduces annotation costs but often suffers from incomplete target activation. To address this, we propose CAME(Cross-Attentive Multimodal Enhancement for Weakly-Supervised Referring Image Semantic Segmentation). CAME introduces CrossModalityAttnFusion(CMAF) to guide language features using visual cues, thereby improving semantic alignment. A Positional Encoding 2D(PED) module enhances structural perception through coordinate encoding. Structure-Aware Consistency Loss(SACL) enforces boundary alignment via edge constraints. Additionally, Entropy Constraint Loss and Focal Loss V2 suppress low-confidence noise and emphasize difficult regions. This joint optimization improves pseudo mask quality, resulting in sharper boundaries and enhanced regional consistency. Experiments show that CAME achieves mIoU scores of 54.96%, 57.08%, and 53.01% on the validation and test sets of RefCOCO, 48.25%, 46.37%, and 45.09% on RefCOCO+, and 54.89% on the validation set of RefCOCOg, significantly outperforming existing weakly supervised methods. These results confirm the effectiveness and strong generalization ability of the proposed approach.