Self-Error Estimation and Dual Refinement Plug-in for High-Quality Instance Segmentation

Abstract

Instance segmentation is a crucial task in computer vision with numerous applications. Recent approaches incorporating refinement modules have shown promise. However, their reliance on indirect guidance often results in suboptimal performance and high computational costs.We introduce the self-error estimation and dual refinement (SEED), a simple yet powerful plug-in module that significantly enhances instance segmentation quality through explicit fine-grained guidance. SEED identifies proposed self-error, defined as pixel-wise discrepancies between initial predictions and ground truth. It then leverages this information to guide the dual refinement of bounding boxes and masks. SEED seamlessly integrates into various existing methods by being added as a plug-in, and enables targeted and effective improvements in both detection and segmentation quality. Extensive experiments demonstrate that SEED significantly improves the performance of state-of-the-art models across diverse datasets, including natural, driving, and robotic scenes while maintaining computational efficiency, outperforming existing refinement-based high-quality instance segmentation methods. The code is available at https://github.com/gist-ailab/seed. © 2013 IEEE.