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arXiv · 2504.05137

BoxSeg: Quality-Aware and Peer-Assisted Learning for Box-supervised Instance Segmentation

Jinxiang Lai·Wenlong Wu·Jiawei Zhan·Jian Li·Bin-Bin Gao·Jun Liu·Jie Zhang·Song Guo
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Abstract

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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Jinxiang Lai, Wenlong Wu, Jiawei Zhan, Jian Li, Bin-Bin Gao, Jun Liu, Jie Zhang, Song Guo. 2025-04-07. BoxSeg: Quality-Aware and Peer-Assisted Learning for Box-supervised Instance Segmentation. https://arxiv.org/abs/2504.05137

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