DGSeg: Dynamic Gating of Semantic-Spatial Guided Predictions for Reasoning Segmentation

Reasoning segmentation aims to predict pixel-wise masks fortargets given complex language queries. Existing approaches leverageMultimodal Large Language Models (MLLMs) for vision-language rea-soning and generate intermediate target cues (e.g., points or boxes) toguide a segmentation model. However, compressing rich reasoning intosparse cues often introduces ambiguity and noise, preventing these cuesfrom accurately preserving the reasoning intent. While multiple comple-mentary cues can enrich target information, existing methods typicallyfeed them jointly into a single segmentation process, allowing ambiguousor erroneous cues to affect the entire prediction. Therefore, we proposeDGSeg, a reasoning segmentation framework that learns to fuse pre-dictions guided by semantic and spatial cues. Specifically, the MLLMjointly reasons about both target identity and spatial location, produc-ing complementary semantic and spatial cues that are fed into separatesegmentation branches. Their predictions are adaptively integrated by alightweight dynamic gating module trained with relative branch-qualitysupervision to suppress noisy or conflicting regions. Extensive experi-ments demonstrate that DGSeg consistently outperforms strong base-lines on multiple benchmarks and achieves 69.6% and 67.3% gIoU on thechallenging ReasonSeg validation and test splits.