发表机构
Ruijin-XJTLU Intelligent Medicine Institute, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine; School of Biomedical Engineering, Shanghai Jiao Tong University（瑞金-西交利物浦智能医学研究所，瑞金医院，上海交通大学医学院; 上海交通大学生物医学工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出SRPR-Net，通过顺序提示精炼机制融合视觉-语言语义与实例间依赖，实现基于SAM的自动实例分割，在多个基准上超越现有最先进方法。
AI 中文摘要
实例分割是一项基础的计算机视觉任务，具有多样化的现实应用。近年来，提示驱动的基础模型展现出良好的泛化能力。然而，自动提示仍受限于语义引导不足和实例间建模缺失。为应对这一挑战，我们提出了一种新颖架构，命名为语义关系提示精炼网络（SRPR-Net），用于基于SAM的自动实例分割。我们引入了一种顺序提示精炼机制，以视觉-语言语义丰富检测器几何信息，并进一步融入同图像实例依赖关系，从而在SAM分割前实现上下文感知的框调整。在多个标准基准上的实验表明，SRPR-Net在分割性能上相较于现有最先进方法取得了持续改进。代码已公开于该https URL。
英文摘要
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.
