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SeGDeP: Semantic- and Geometric-Aware Decoupled Prompts for Reasoning Segmentation

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

Reasoning segmentation converts an implicit linguistic conclusion into a precise mask, requiring both semantic identification and spatial grounding. Existing MLLM-segmenter interfaces either use a special trigger or compress both signals into one context, although they receive different supervision and fail differently. This coupling obscures whether a failure arises from target interpretation or from localization. We present SeGDeP, an explicit what-where interface. A semantic prompt branch and an independent geometric projection path transform resolved MLLM states into semantic features and

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