Recently, inspired by DETR variants, query-based end-to-end instance
segmentation (QEIS) methods have outperformed CNN-based models on large-scale
datasets. Yet they would lose efficacy when only a small amount of training
data is available since it's hard for the crucial queries/kernels to learn
localization and shape priors. To this end, this work offers a novel
unsupervised pre-training solution for low-data regimes. Inspired by the recent
success of the Prompting technique, we introduce a new pre-training method that
boosts QEIS models by giving Saliency Prompt for queries/kernels. Our method
contains three parts: 1) Saliency Masks Proposal is responsible for generating
pseudo masks from unlabeled images based on the saliency mechanism. 2)
Prompt-Kernel Matching transfers pseudo masks into prompts and injects the
corresponding localization and shape priors to the best-matched kernels. 3)
Kernel Supervision is applied to supply supervision at the kernel level for
robust learning. From a practical perspective, our pre-training method helps
QEIS models achieve a similar convergence speed and comparable performance with
CNN-based models in low-data regimes. Experimental results show that our method
significantly boosts several QEIS models on three datasets. Code will be made
available.

本研究提出了一种基于未标注图像的显著性策略和 Prompting 技术的无监督预训练方法，包括三个步骤：显著性掩码提取、Prompt-Kernel 匹配和核监督。在低数据环境中有效地提高了 QEIS 模型的性能和收敛速度。