A$^2$LC: Active and Automated Label Correction for Semantic Segmentation

TL;DR

A$^2$LC is a novel framework that combines active and automated label correction with human feedback to improve semantic segmentation accuracy efficiently, significantly reducing annotation costs and outperforming previous methods.

Contribution

The paper introduces A$^2$LC, a cascaded correction framework with an adaptive acquisition function that emphasizes tail classes, enhancing correction efficiency and segmentation performance.

Findings

Outperforms previous state-of-the-art methods on Cityscapes and PASCAL VOC 2012.

Achieves 27.23% performance gain with only 20% of the previous budget.

Demonstrates high efficiency and effectiveness in label correction for semantic segmentation.

Abstract

Active Label Correction (ALC) has emerged as a promising solution to the high cost and error-prone nature of manual pixel-wise annotation in semantic segmentation, by actively identifying and correcting mislabeled data. Although recent work has improved correction efficiency by generating pseudo-labels using foundation models, substantial inefficiencies still remain. In this paper, we introduce A$^2$LC, an Active and Automated Label Correction framework for semantic segmentation, where manual and automatic correction stages operate in a cascaded manner. Specifically, the automatic correction stage leverages human feedback to extend label corrections beyond the queried samples, thereby maximizing cost efficiency. In addition, we introduce an adaptively balanced acquisition function that emphasizes underrepresented tail classes, working in strong synergy with the automatic correction stage. Extensive experiments on Cityscapes and PASCAL VOC 2012 demonstrate that A$^2$LC significantly outperforms previous state-of-the-art methods. Notably, A$^2$LC exhibits high efficiency by outperforming previous methods with only 20% of their budget, and shows strong effectiveness by achieving a 27.23% performance gain under the same budget on Cityscapes.