  Segmenting and recognizing diverse object parts is crucial in computer vision
and robotics. Despite significant progress in object segmentation, part-level
segmentation remains underexplored due to complex boundaries and scarce
annotated data. To address this, we propose a novel Weakly-supervised Part
Segmentation (WPS) setting and an approach called WPS-SAM, built on the
large-scale pre-trained vision foundation model, Segment Anything Model (SAM).
WPS-SAM is an end-to-end framework designed to extract prompt tokens directly
from images and perform pixel-level segmentation of part regions. During its
training phase, it only uses weakly supervised labels in the form of bounding
boxes or points. Extensive experiments demonstrate that, through exploiting the
rich knowledge embedded in pre-trained foundation models, WPS-SAM outperforms
other segmentation models trained with pixel-level strong annotations.
Specifically, WPS-SAM achieves 68.93% mIOU and 79.53% mACC on the PartImageNet
dataset, surpassing state-of-the-art fully supervised methods by approximately
4% in terms of mIOU.
