GroundingSuite: Measuring Complex Multi-Granular Pixel Grounding
This addresses the problem of limited and low-quality datasets for pixel grounding tasks, which is incremental as it builds on existing annotation methods.
The paper tackles limitations in pixel grounding datasets by introducing GroundingSuite, an automated annotation framework that creates a large-scale training dataset, resulting in state-of-the-art performance with a cIoU of 68.9 on gRefCOCO and a gIoU of 55.3 on RefCOCOm.
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.