mlsanityData to 14 Oct 2026

A2LC: Active and Automated Label Correction for Semantic Segmentation

arXiv 2506.11599 ↗First posted 13 Jun 2025cs.CV cs.AIcited by 2

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Abstract (arXiv)

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 A2LC, 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 A2LC significantly outperforms previous state-of-the-art methods. Notably, A2LC 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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 39 of its 57 references that are in the map. First posted on arXiv 13 Jun 2025.

Builds on

39 of 57 references.

References · 39

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27 more
  1. 2021Interactive Label Cleaning with Example-based Explanationscited by 49
  2. 2021Instance-Dependent Partial Label Learningcited by 49
  3. 2021Active label cleaning for improved dataset quality under resource constraintscited by 44
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025From Label Error Detection to Correction: A Modular Framework and Benchmark for Object Detection Datasetscited by 0
  2. 2026iSAGE: A Human-in-the-Loop Framework for Remote Sensing Semantic Segmentation via Sparse Point Supervisioncited by 0