  In this work, we present a robust approach for joint part and object
segmentation. Specifically, we reformulate object and part segmentation as an
optimization problem and build a hierarchical feature representation including
pixel, part, and object-level embeddings to solve it in a bottom-up clustering
manner. Pixels are grouped into several clusters where the part-level
embeddings serve as cluster centers. Afterwards, object masks are obtained by
compositing the part proposals. This bottom-up interaction is shown to be
effective in integrating information from lower semantic levels to higher
semantic levels. Based on that, our novel approach Compositor produces part and
object segmentation masks simultaneously while improving the mask quality.
Compositor achieves state-of-the-art performance on PartImageNet and
Pascal-Part by outperforming previous methods by around 0.9% and 1.3% on
PartImageNet, 0.4% and 1.7% on Pascal-Part in terms of part and object mIoU and
demonstrates better robustness against occlusion by around 4.4% and 7.1% on
part and object respectively. Code will be available at
https://github.com/TACJu/Compositor.
