GroundingSuite: Measuring Complex Multi-Granular Pixel Grounding
Authors: name: Rui Hu, name: Lianghui Zhu, name: Yuxuan Zhang, name: Tianheng Cheng, name: Lei Liu, name: Heng Liu, name: Longjin Ran, name: Xiaoxin Chen, name: Wenyu Liu, name: Xinggang Wang

Pixel grounding, encompassing tasks such as Referring Expression Segmentation
(RES), has garnered considerable attention due to its immense potential for
bridging the gap between vision and language modalities. However, advancements
in this domain are currently constrained by limitations inherent in existing
datasets, including limited object categories, insufficient textual diversity,
and a scarcity of high-quality annotations. To mitigate these limitations, we
introduce GroundingSuite, which comprises: (1) an automated data annotation
framework leveraging multiple Vision-Language Model (VLM) agents; (2) a
large-scale training dataset encompassing 9.56 million diverse referring
expressions and their corresponding segmentations; and (3) a meticulously
curated evaluation benchmark consisting of 3,800 images. The GroundingSuite
training dataset facilitates substantial performance improvements, enabling
models trained on it to achieve state-of-the-art results. Specifically, a cIoU
of 68.9 on gRefCOCO and a gIoU of 55.3 on RefCOCOm. Moreover, the
GroundingSuite annotation framework demonstrates superior efficiency compared
to the current leading data annotation method, i.e., 4.5 times faster than
the GLaMM.