  In this work, we address the task of few-shot part segmentation, which aims
to segment the different parts of an unseen object using very few labeled
examples. It is found that leveraging the textual space of a powerful
pre-trained image-language model (such as CLIP) can be beneficial in learning
visual features. Therefore, we develop a novel method termed PartSeg for
few-shot part segmentation based on multimodal learning. Specifically, we
design a part-aware prompt learning method to generate part-specific prompts
that enable the CLIP model to better understand the concept of ``part'' and
fully utilize its textual space. Furthermore, since the concept of the same
part under different object categories is general, we establish relationships
between these parts during the prompt learning process. We conduct extensive
experiments on the PartImageNet and Pascal$\_$Part datasets, and the
experimental results demonstrated that our proposed method achieves
state-of-the-art performance.
