Active Label Correction for Semantic Segmentation with Foundation Models

• URL: http://arxiv.org/abs/2403.10820v2
• Date: Tue, 4 Jun 2024 13:15:16 GMT
• Title: Active Label Correction for Semantic Segmentation with Foundation Models
• Authors: Hoyoung Kim, Sehyun Hwang, Suha Kwak, Jungseul Ok,
• Abstract summary: We propose an effective framework of active label correction (ALC) based on a design of correction query to rectify pseudo labels of pixels. 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.
• Score: 34.0733215363568
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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