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

TL;DR

This paper introduces a novel framework for semi-supervised instance segmentation that decouples and corrects pseudo-label quality at multiple levels, significantly improving performance especially with limited labeled data.

Contribution

The proposed PL-DC framework uniquely decouples class and mask quality assessments, dynamically corrects category labels, and re-weights mask loss at the pixel level, advancing SSIS methods.

Findings

Achieves state-of-the-art results on COCO and Cityscapes datasets.

Substantial performance gains with minimal labeled data (+11.6 mAP with 1% COCO data).

Effective noise reduction in pseudo-labels through multi-level correction mechanisms.

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.