The paper introduces PaintSeg, a new unsupervised method for segmenting
objects without any training. We propose an adversarial masked contrastive
painting (AMCP) process, which creates a contrast between the original image
and a painted image in which a masked area is painted using off-the-shelf
generative models. During the painting process, inpainting and outpainting are
alternated, with the former masking the foreground and filling in the
background, and the latter masking the background while recovering the missing
part of the foreground object. Inpainting and outpainting, also referred to as
I-step and O-step, allow our method to gradually advance the target
segmentation mask toward the ground truth without supervision or training.
PaintSeg can be configured to work with a variety of prompts, e.g. coarse
masks, boxes, scribbles, and points. Our experimental results demonstrate that
PaintSeg outperforms existing approaches in coarse mask-prompt, box-prompt, and
point-prompt segmentation tasks, providing a training-free solution suitable
for unsupervised segmentation.

本篇论文提出了一种无监督分割方法，使用了基于对抗过程的遮罩对比上色技术，通过 inpainting 和 outpainting 逐步接近真实目标分割遮罩。实验结果表明该方法在不同类型的分割任务方面表现都优于现有方法。