GroundingSuite：测量复杂多粒度像素定位
GroundingSuite: Measuring Complex Multi-Granular Pixel Grounding
AI总结：
本文提出GroundingSuite，包含基于VLM智能体的自动标注框架、956万条指代表达式的大规模训练集及3800张图像的评估基准，显著提升了像素定位性能并实现比GLaMM快4.5倍的标注效率。
摘要：
像素定位涵盖诸如指代表达式分割（RES）等任务，因其在弥合视觉与语言模态之间差距的巨大潜力而备受关注。然而，该领域的进展目前受限于现有数据集固有的缺陷，包括目标类别有限、文本多样性不足以及高质量标注稀缺。为缓解这些限制，我们引入了GroundingSuite，它包含：(1) 利用多个视觉-语言模型（VLM）智能体的自动化数据标注框架；(2) 包含956万条多样化指代表达式及其对应分割结果的大规模训练数据集；(3) 由3,800张图像精心策划的评估基准。GroundingSuite训练数据集促进了显著的性能提升，使基于其训练的模型取得了最先进的结果。具体而言，在gRefCOCO上cIoU为68.9，在RefCOCOm上gIoU为55.3。此外，与当前领先的数据标注方法相比，GroundingSuite标注框架展现出更高的效率，即比GLaMM快4.5倍。
英文摘要：
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 GLaMM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
