Few-shot part segmentation aims to separate different parts of an object given only a few annotated samples. Due to the challenge of limited data, existing works mainly focus on learning classifiers over pre-trained features, failing to learn task-specific features for part segmentation. In this paper, we propose to learn task-specific features in a "pre-training"-"fine-tuning" paradigm. We conduct prompt designing to reduce the gap between the pre-train task (i.e., image generation) and the downstream task (i.e., part segmentation), so that the GAN priors for generation can be leveraged for segmentation. This is achieved by projecting part segmentation maps into the RGB space and conducting interpolation between RGB segmentation maps and original images. Specifically, we design a fine-tuning strategy to progressively tune an image generator into a segmentation generator, where the supervision of the generator varying from images to segmentation maps by interpolation. Moreover, we propose a two-stream architecture, i.e., a segmentation stream to generate task-specific features, and an image stream to provide spatial constraints. The image stream can be regarded as a self-supervised auto-encoder, and this enables our model to benefit from large-scale support images. Overall, this work is an attempt to explore the internal relevance between generation tasks and perception tasks by prompt designing. Extensive experiments show that our model can achieve state-of-the-art performance on several part segmentation datasets.

翻译：由于数据有限,现有工作主要侧重于在培训前的特性上学习分类,没有为部分分解学习任务特性。在本文中,我们提议在“培训前”-“调整”范式中学习任务特性。我们进行迅速设计,以减少培训前任务(即图像生成)与下游任务(即部分分解)之间的差距,从而能够利用GAN前代任务(即部分分解)进行分解。这主要是通过将部分分解图投入RGB空间,并在 RGB 分解图和原始图像之间进行内推,来实现的。具体地说,我们设计了一个微调战略,将图像生成器逐步调整成一个分解生成器,使发电机的监督从图像生成到分解图之间差异不等。此外,我们提出一个双流结构,即分解流以生成特定任务生成分解,以及图像流提供空间分解内部分解图的分解图流,通过提供空间分解内部分解的分解图流来达到部分分解。具体地显示一个图像流,通过将我们这一图像流转化为的图像流转化为,从而显示整个图像流显示一个比例任务,从而显示我们的一个自动浏览,可以被视为一个可实现整个任务。