Pseudo-label Alignment for Semi-supervised Instance Segmentation
Jie Hu Chen Chen Liujuan Cao Shengchuan Zhang Annan Shu Guannan Jiang Rongrong Ji
Pseudo-labeling is significant for semi-supervised instance segmentation,
which generates instance masks and classes from unannotated images for
subsequent training. However, in existing pipelines, pseudo-labels that contain
valuable information may be directly filtered out due to mismatches in class
and mask quality. To address this issue, we propose a novel framework, called
pseudo-label aligning instance segmentation (PAIS), in this paper. In PAIS, we
devise a dynamic aligning loss (DALoss) that adjusts the weights of
semi-supervised loss terms with varying class and mask score pairs. Through
extensive experiments conducted on the COCO and Cityscapes datasets, we
demonstrate that PAIS is a promising framework for semi-supervised instance
segmentation, particularly in cases where labeled data is severely limited.
Notably, with just 1\% labeled data, PAIS achieves 21.2 mAP (based on
Mask-RCNN) and 19.9 mAP (based on K-Net) on the COCO dataset, outperforming the
current state-of-the-art model, \ie, NoisyBoundary with 7.7 mAP, by a margin of
over 12 points. Code is available at: \url{https://github.com/hujiecpp/PAIS}.