Going Denser with Open-Vocabulary Part Segmentation
Peize Sun Shoufa Chen Chenchen Zhu Fanyi Xiao Ping Luo Saining Xie Zhicheng Yan
Object detection has been expanded from a limited number of categories to
open vocabulary. Moving forward, a complete intelligent vision system requires
understanding more fine-grained object descriptions, object parts. In this
paper, we propose a detector with the ability to predict both open-vocabulary
objects and their part segmentation. This ability comes from two designs.
First, we train the detector on the joint of part-level, object-level and
image-level data to build the multi-granularity alignment between language and
image. Second, we parse the novel object into its parts by its dense semantic
correspondence with the base object. These two designs enable the detector to
largely benefit from various data sources and foundation models. In
open-vocabulary part segmentation experiments, our method outperforms the
baseline by 3.3$\sim$7.3 mAP in cross-dataset generalization on PartImageNet,
and improves the baseline by 7.3 novel AP$_{50}$ in cross-category
generalization on Pascal Part. Finally, we train a detector that generalizes to
a wide range of part segmentation datasets while achieving better performance
than dataset-specific training.