Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation

• URL: http://arxiv.org/abs/2505.11075v1
• Date: Fri, 16 May 2025 10:07:17 GMT
• Title: Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation
• Authors: Jianghang Lin, Yilin Lu, Yunhang Shen, Chaoyang Zhu, Shengchuan Zhang, Liujuan Cao, Rongrong Ji,
• Abstract summary: Semi-Supervised Instance (SSIS) involves classifying and grouping image pixels into distinct object instances.<n>This learning paradigm usually faces a significant challenge of unstable performance caused by noisy pseudo-labels of instance categories and pixel masks.<n>We introduce a novel PseudoLabel Quality Decoupling and Correction (PL-DC) framework for tackling the above challenges.
• Score: 62.55963720723179
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semi-Supervised Instance Segmentation (SSIS) involves classifying and grouping image pixels into distinct object instances using limited labeled data. This learning paradigm usually faces a significant challenge of unstable performance caused by noisy pseudo-labels of instance categories and pixel masks. We find that the prevalent practice of filtering instance pseudo-labels assessing both class and mask quality with a single score threshold, frequently leads to compromises in the trade-off between the qualities of class and mask labels. In this paper, we introduce a novel Pseudo-Label Quality Decoupling and Correction (PL-DC) framework for SSIS to tackle the above challenges. Firstly, at the instance level, a decoupled dual-threshold filtering mechanism is designed to decouple class and mask quality estimations for instance-level pseudo-labels, thereby independently controlling pixel classifying and grouping qualities. Secondly, at the category level, we introduce a dynamic instance category correction module to dynamically correct the pseudo-labels of instance categories, effectively alleviating category confusion. Lastly, we introduce a pixel-level mask uncertainty-aware mechanism at the pixel level to re-weight the mask loss for different pixels, thereby reducing the impact of noise introduced by pixel-level mask pseudo-labels. Extensive experiments on the COCO and Cityscapes datasets demonstrate that the proposed PL-DC achieves significant performance improvements, setting new state-of-the-art results for SSIS. Notably, our PL-DC shows substantial gains even with minimal labeled data, achieving an improvement of +11.6 mAP with just 1% COCO labeled data and +15.5 mAP with 5% Cityscapes labeled data. The code will be public.

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