  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.
