SRPR-Net: Semantic and Relational Prompt Refinement for Automated SAM-based Instance Segmentation

2026-09-21

AI Summary

为解决自动提示在实例分割中的语义指导不足和实例间建模问题，提出SRPR-Net，通过视觉-语言语义增强和实例依赖关系建模来改进SAM的分割性能。

Abstract

Instance segmentation is a fundamental computer vision task with diverse real-world applications. Recently, prompt-driven foundation models have shown promising generalization. However, automated prompting remains limited by insufficient semantic guidance and inter-instance modeling. To address this challenge, we propose a novel architecture, named Semantic Relational Prompt Refinement Network (SRPR-Net), for automated SAM-based instance segmentation. A sequential prompt refinement mechanism is introduced to enrich detector geometry with visual-language semantics and then incorporate same-image instance dependencies, enabling context-aware box adjustment before SAM segmentation. Experiments on multiple standard benchmarks demonstrate that SRPR-Net achieves consistent improvements in segmentation performance over existing state-of-the-art approaches. The code is publicly available at https://github.com/JeremyXSC/SRPR-Net.

Problem

• instance segmentation
• prompt-driven foundation models
• semantic guidance
• inter-instance modeling

Innovation

• Semantic Relational Prompt Refinement
• automated SAM-based instance segmentation
• sequential prompt refinement mechanism
• context-aware box adjustment