GroundingSuite: Measuring Complex Multi-Granular Pixel Grounding
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.
67d3a8950ada3dfbf617fc23
Rui Hu
admin_assigned
2025-03-14T10:45:13.299Z
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Rui Hu
user
RuiHu
67d3a8950ada3dfbf617fc24
Lianghui Zhu
admin_assigned
2025-03-14T10:45:06.399Z
643632c6a4bd75c62cc2d3b4
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Lianghui Zhu
user
LianghuiZhu
67d3a8950ada3dfbf617fc25
Yuxuan Zhang
67d3a8950ada3dfbf617fc26
Tianheng Cheng
admin_assigned
2025-03-14T10:44:40.138Z
646b3db131968a60a01e4cf5
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Tianheng Cheng
user
wondervictor
67d3a8950ada3dfbf617fc27
Lei Liu
67d3a8950ada3dfbf617fc28
Heng Liu
67d3a8950ada3dfbf617fc29
Longjin Ran
67d3a8950ada3dfbf617fc2a
Xiaoxin Chen
admin_assigned
2025-03-14T10:44:23.180Z
65389a669c474315d7425f96
/avatars/2fa3828ca489cfe1948129a0eccf264f.svg
chenxiaoxin
user
steelozazala
67d3a8950ada3dfbf617fc2b
Wenyu Liu
admin_assigned
2025-03-14T10:43:44.312Z
66c2e7fc934e2f07753542ac
/avatars/f6fa3f94435cf1c1d06daa6c925d07d0.svg
LWY
user
wenyuliu
67d3a8950ada3dfbf617fc2c
Xinggang Wang
admin_assigned
2025-03-14T10:43:37.668Z
62600de6d47e3dbae32ce1ce
/avatars/a536417cfec6e10ac415091bd1829426.svg
Xinggang Wang
user
xinggangw
GroundingSuite: Measuring Complex Multi-Granular Pixel Grounding
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.
GroundingSuite improves pixel grounding by introducing a large-scale dataset and automated annotation framework, enhancing model performance and efficiency.
Vision-Language Model VLM Referring Expression Segmentation RES gIoU GroundingSuite GLaMM