  We present PartGLEE, a part-level foundation model for locating and
identifying both objects and parts in images. Through a unified framework,
PartGLEE accomplishes detection, segmentation, and grounding of instances at
any granularity in the open world scenario. Specifically, we propose a Q-Former
to construct the hierarchical relationship between objects and parts, parsing
every object into corresponding semantic parts. By incorporating a large amount
of object-level data, the hierarchical relationships can be extended, enabling
PartGLEE to recognize a rich variety of parts. We conduct comprehensive studies
to validate the effectiveness of our method, PartGLEE achieves the
state-of-the-art performance across various part-level tasks and obtain
competitive results on object-level tasks. The proposed PartGLEE significantly
enhances hierarchical modeling capabilities and part-level perception over our
previous GLEE model. Further analysis indicates that the hierarchical cognitive
ability of PartGLEE is able to facilitate a detailed comprehension in images
for mLLMs. The model and code will be released at
https://provencestar.github.io/PartGLEE-Vision/ .
