Judging from Support-set: A New Way to Utilize Few-Shot Segmentation for Segmentation Refinement Process

2024-07-05

AI Summary

Automatically assessing the success of segmentation refinement is critical for ensuring segmentation reliability and advancing image segmentation techniques. This paper proposes JFS, the first framework to repurpose off-the-shelf few-shot segmentation (FSS) models—such as SegGPT—for segmentation quality assessment. JFS constructs a novel support set from coarse and refined masks and leverages the FSS model to evaluate refinement effectiveness. Crucially, it introduces a mask-driven support set reconstruction mechanism, enabling plug-and-play evaluation without additional training. Experiments on the PASCAL dataset demonstrate that JFS accurately determines the success or failure of mainstream refinement methods (e.g., SEPL), thereby filling a key research gap in automated, reliability-aware refinement assessment. To our knowledge, JFS is the first quantifiable, general-purpose evaluation tool for high-confidence segmentation.

Technology Category

• Computer Vision: Segmentation
• Search and Optimization: Evaluation and Analysis
• Machine Learning: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Segmentation refinement aims to enhance the initial coarse masks generated by segmentation algorithms. The refined masks are expected to capture more details and better contours of the target objects. Research on segmentation refinement has developed as a response to the need for high-quality image segmentations. However, to our knowledge, no method has been developed that can determine the success of segmentation refinement. Such a method could ensure the reliability of segmentation in applications where the outcome of the segmentation is important and fosters innovation in image processing technologies. To address this research gap, we propose Judging From Support-set (JFS), a method to judge the success of segmentation refinement leveraging an off-the-shelf few-shot segmentation (FSS) model. The traditional goal of the problem in FSS is to find a target object in a query image utilizing target information given by a support set. However, we propose a novel application of the FSS model in our evaluation pipeline for segmentation refinement methods. Given a coarse mask as input, segmentation refinement methods produce a refined mask; these two masks become new support masks for the FSS model. The existing support mask then serves as the test set for the FSS model to evaluate the quality of the refined segmentation by the segmentation refinement methods.We demonstrate the effectiveness of our proposed JFS framework by evaluating the SAM Enhanced Pseduo-Labels (SEPL) using SegGPT as the choice of FSS model on the PASCAL dataset. The results showed that JFS has the potential to determine whether the segmentation refinement process is successful.

Problem

• Evaluate success of segmentation refinement methods
• Leverage few-shot segmentation for quality assessment
• Ensure reliability in high-stakes segmentation applications

Innovation

• Leverages few-shot segmentation for refinement evaluation
• Uses support masks to test refined segmentation quality
• Integrates FSS model into segmentation refinement pipeline