A$^2$LC: Active and Automated Label Correction for Semantic Segmentation
This work addresses the problem of expensive and inaccurate labeling in semantic segmentation for computer vision researchers and practitioners, representing an incremental improvement over existing active label correction methods.
The paper tackles the high cost and error-prone nature of manual pixel-wise annotation in semantic segmentation by proposing A^2LC, an active and automated label correction framework that integrates automated correction and an adaptively balanced acquisition function. It significantly outperforms previous state-of-the-art methods, achieving high efficiency with only 20% of their budget and a 27.23% performance improvement under an equivalent budget on Cityscapes.
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 selectively 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 propose Active and Automated Label Correction for semantic segmentation (A$^2$LC), a novel and efficient ALC framework that integrates an automated correction stage into the conventional pipeline. Specifically, the automated correction stage leverages annotator feedback to perform label correction beyond the queried samples, thereby maximizing cost efficiency. In addition, we further introduce an adaptively balanced acquisition function that emphasizes underrepresented tail classes and complements the automated correction mechanism. 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 achieves high efficiency by outperforming previous methods using only 20% of their budget, and demonstrates strong effectiveness by yielding a 27.23% performance improvement under an equivalent budget constraint on the Cityscapes dataset. The code will be released upon acceptance.