FoRIS: Progressive Foreground Refinement for Training-Free In-Context Segmentation

2026-09-03

AI Summary

本文针对任意语义概念的精确分割问题，提出了一种无需训练的上下文分割框架FoRIS，通过逐步细化前景响应达到精准分割。

Abstract

In-Context Segmentation (ICS) aims to precisely segment arbitrary semantic concepts, such as objects or parts, given one or a few annotated visual exemplars. In this paper, we revisit ICS from a more classical segmentation perspective, viewing it as a coarse-to-fine progressive refinement process. Rather than directly predicting the final mask through reference-query matching, we progressively refine the segmentation from coarse and ambiguous foreground responses to precise and complete foreground structures. Building upon this perspective, we propose a training-free in-context segmentation framework, termed FoRIS. Specifically, FoRIS consists of three key stages: Foreground Purification, Foreground Localization, and Foreground Consolidation, which progressively suppress background distractions, localize discriminative target regions, and recover complete foreground structures through semantic aggregation. Experimental results demonstrate that FoRIS achieves SOTA performance across semantic and part segmentation tasks, with average improvements of 4.5 and 4.8 mIoU points over existing approaches in the 1-shot and 5-shot settings, respectively. Code: https://github.com/Xi-Mu-Yu/FoRIS.

Problem

• In-Context Segmentation
• Foreground Refinement
• Semantic Concepts

Innovation

• In-Context Segmentation
• Foreground Refinement
• Training-Free
• Coarse-to-Fine
• Semantic Aggregation