Progressive Prompt-Guided Cross-Modal Reasoning for Referring Image Segmentation

TL;DR

PPCR introduces a progressive reasoning framework that combines large language models and prompt-guided spatial grounding to improve referring image segmentation accuracy.

Contribution

It proposes a novel structured reasoning pipeline with semantic and spatial prompts, enhancing cross-modal grounding in image segmentation tasks.

Findings

PPCR outperforms existing methods on standard benchmarks.

The framework effectively bridges semantic understanding and spatial reasoning.

Code will be publicly released for reproducibility.

Abstract

Referring image segmentation aims to localize and segment a target object in an image based on a free-form referring expression. The core challenge lies in effectively bridging linguistic descriptions with object-level visual representations, especially when referring expressions involve detailed attributes and complex inter-object relationships. Existing methods either rely on cross-modal alignment or employ Semantic Segmentation Prompts, but they often lack explicit reasoning mechanisms for grounding language descriptions to target regions in the image. To address these limitations, we propose PPCR, a Progressive Prompt-guided Cross-modal Reasoning framework for referring image segmentation. PPCR explicitly structures the reasoning process as a Semantic Understanding-Spatial Grounding-Instance Segmentation pipeline. Specifically, PPCR first employs multimodal large language models (MLLMs) to generate Semantic Segmentation Prompt that capture key semantic cues of the target object. Based on this semantic context, Spatial Segmentation Prompt are further generated to reason about object location and spatial extent, enabling a progressive transition from semantic understanding to spatial grounding. The Semantic and Spatial Segmentation prompts are then jointly integrated into the segmentation module to guide accurate target localization and segmentation. Extensive experiments on standard referring image segmentation benchmarks demonstrate that PPCR consistently outperforms existing methods. The code will be publicly released to facilitate reproducibility.