title: GroundingSuite: Measuring Complex Multi-Granular Pixel Grounding
summary: 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.
paper_authors._id: 67d3a8950ada3dfbf617fc23
paper_authors.name: Rui Hu
paper_authors.status: admin_assigned
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paper_authors.fullname: Rui Hu
paper_authors.type: user
paper_authors.user: RuiHu
paper_authors._id: 67d3a8950ada3dfbf617fc24
paper_authors.name: Lianghui Zhu
paper_authors.status: admin_assigned
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paper_authors.fullname: Lianghui Zhu
paper_authors.type: user
paper_authors.user: LianghuiZhu
paper_authors._id: 67d3a8950ada3dfbf617fc25
paper_authors.name: Yuxuan Zhang
paper_authors._id: 67d3a8950ada3dfbf617fc26
paper_authors.name: Tianheng Cheng
paper_authors.status: admin_assigned
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paper_authors.fullname: Tianheng Cheng
paper_authors.type: user
paper_authors.user: wondervictor
paper_authors._id: 67d3a8950ada3dfbf617fc27
paper_authors.name: Lei Liu
paper_authors._id: 67d3a8950ada3dfbf617fc28
paper_authors.name: Heng Liu
paper_authors._id: 67d3a8950ada3dfbf617fc29
paper_authors.name: Longjin Ran
paper_authors._id: 67d3a8950ada3dfbf617fc2a
paper_authors.name: Xiaoxin Chen
paper_authors.status: admin_assigned
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paper_authors._id: 65389a669c474315d7425f96
paper_authors.avatarUrl: /avatars/2fa3828ca489cfe1948129a0eccf264f.svg
paper_authors.fullname: chenxiaoxin
paper_authors.type: user
paper_authors.user: steelozazala
paper_authors._id: 67d3a8950ada3dfbf617fc2b
paper_authors.name: Wenyu Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T10:43:44.312Z
paper_authors._id: 66c2e7fc934e2f07753542ac
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paper_authors.fullname: LWY
paper_authors.type: user
paper_authors.user: wenyuliu
paper_authors._id: 67d3a8950ada3dfbf617fc2c
paper_authors.name: Xinggang Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T10:43:37.668Z
paper_authors._id: 62600de6d47e3dbae32ce1ce
paper_authors.avatarUrl: /avatars/a536417cfec6e10ac415091bd1829426.svg
paper_authors.fullname: Xinggang Wang
paper_authors.type: user
paper_authors.user: xinggangw
paper_title: GroundingSuite: Measuring Complex Multi-Granular Pixel Grounding
paper_summary: 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.
paper_ai_summary: GroundingSuite improves pixel grounding by introducing a large-scale dataset and automated annotation framework, enhancing model performance and efficiency.
paper_ai_keywords: Vision-Language Model VLM Referring Expression Segmentation RES gIoU GroundingSuite GLaMM