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., faster than the GLaMM.
View on arXiv@article{hu2025_2503.10596,
title={ GroundingSuite: Measuring Complex Multi-Granular Pixel Grounding },
author={ Rui Hu and Lianghui Zhu and Yuxuan Zhang and Tianheng Cheng and Lei Liu and Heng Liu and Longjin Ran and Xiaoxin Chen and Wenyu Liu and Xinggang Wang },
journal={arXiv preprint arXiv:2503.10596},
year={ 2025 }
}