用高质量伪掩码和同伴辅助提升框标注下的实例分割性能
BoxSeg: Quality-Aware and Peer-Assisted Learning for Box-supervised Instance Segmentation
- 引入质量感知模块与同伴复制粘贴机制，生成更优伪掩码
- 在COCO数据集上达到新高，显著优于现有框标注方法
- 适用于多种框架，尤其适合资源受限的弱监督场景
框标注下的实例分割方法旨在仅使用边界框标注实现实例级分割。近期方法已证明在教师-学生框架下可获取高质量伪掩码。本文提出BoxSeg框架，包含两个新颖且通用的模块：质量感知模块（QAM）和同伴辅助复制粘贴（PC）。QAM通过质量感知多掩码补全机制，生成高质量伪掩码并更准确评估其质量，从而降低噪声掩码影响。PC借鉴同伴学习思想，利用高质量伪掩码指导优化低质量掩码。理论与实验分析表明QAM和PC均有效。大量实验证明BoxSeg优于当前最先进方法，并且QAM与PC可推广至其他模型以提升性能。
原文摘要 · Abstract (English)
Box-supervised instance segmentation methods aim to achieve instance segmentation with only box annotations. Recent methods have demonstrated the effectiveness of acquiring high-quality pseudo masks under the teacher-student framework. Building upon this foundation, we propose a BoxSeg framework involving two novel and general modules named the Quality-Aware Module (QAM) and the Peer-assisted Copy-paste (PC). The QAM obtains high-quality pseudo masks and better measures the mask quality to help reduce the effect of noisy masks, by leveraging the quality-aware multi-mask complementation mechanism. The PC imitates Peer-Assisted Learning to further improve the quality of the low-quality masks with the guidance of the obtained high-quality pseudo masks. Theoretical and experimental analyses demonstrate the proposed QAM and PC are effective. Extensive experimental results show the superiority of our BoxSeg over the state-of-the-art methods, and illustrate the QAM and PC can be applied to improve other models.
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