自动纠错+主动学习，用20%预算提升标注效率与精度
A$^2$LC: Active and Automated Label Correction for Semantic Segmentation
- 分两阶段纠错：先自动修正，再结合人工反馈扩展修正范围
- 在Cityscapes上仅用20%预算就达到27.23%性能提升
- 自适应选择难样本，特别改善小众类别标注质量
主动标注纠错（ALC）为缓解语义分割中像素级标注的高成本与错误问题提供了新思路，通过主动识别并修正错误标签。尽管已有研究利用基础模型生成伪标签提升了纠错效率，但仍存在显著瓶颈。本文提出A$^2$LC框架，采用手动与自动纠错的级联机制：自动阶段借助人工反馈将修正范围扩展至查询样本之外，最大化成本效益；同时引入自适应平衡的选样函数，强化对少数尾部类别的关注，与自动阶段形成协同效应。在Cityscapes和PASCAL VOC 2012上的实验表明，A$^2$LC显著优于现有最优方法。尤其在相同预算下，其在Cityscapes上实现27.23%的性能增益，且仅需前人方法20%的标注预算。
原文摘要 · Abstract (English)
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.
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