Training and validating models for semantic segmentation require datasets
with pixel-wise annotations, which are notoriously labor-intensive. Although
useful priors such as foundation models or crowdsourced datasets are available,
they are error-prone. We hence propose an effective framework of active label
correction (ALC) based on a design of correction query to rectify pseudo labels
of pixels, which in turn is more annotator-friendly than the standard one
inquiring to classify a pixel directly according to our theoretical analysis
and user study. Specifically, leveraging foundation models providing useful
zero-shot predictions on pseudo labels and superpixels, our method comprises
two key techniques: (i) an annotator-friendly design of correction query with
the pseudo labels, and (ii) an acquisition function looking ahead label
expansions based on the superpixels. Experimental results on PASCAL,
Cityscapes, and Kvasir-SEG datasets demonstrate the effectiveness of our ALC
framework, outperforming prior methods for active semantic segmentation and
label correction. Notably, utilizing our method, we obtained a revised dataset
of PASCAL by rectifying errors in 2.6 million pixels in PASCAL dataset.

利用主动标签修正（ALC）框架和基础模型，通过纠正像素的伪标签来提高语义分割模型训练和验证的效果。在 PASCAL、Cityscapes 和 Kvasir-SEG 数据集上的实验结果证明了我们的 ALC 框架的有效性，优于传统的主动语义分割和标签修正方法。