COCONut-PanCap: Joint Panoptic Segmentation and Grounded Captions for
  Fine-Grained Understanding and Generation
Authors: name: Xueqing Deng, name: Qihang Yu, name: Ali Athar, name: Chenglin Yang, name: Linjie Yang, name: Xiaojie Jin, name: Xiaohui Shen, name: Liang-Chieh Chen

This paper introduces the COCONut-PanCap dataset, created to enhance panoptic
segmentation and grounded image captioning. Building upon the COCO dataset with
advanced COCONut panoptic masks, this dataset aims to overcome limitations in
existing image-text datasets that often lack detailed, scene-comprehensive
descriptions. The COCONut-PanCap dataset incorporates fine-grained,
region-level captions grounded in panoptic segmentation masks, ensuring
consistency and improving the detail of generated captions. Through
human-edited, densely annotated descriptions, COCONut-PanCap supports improved
training of vision-language models (VLMs) for image understanding and
generative models for text-to-image tasks. Experimental results demonstrate
that COCONut-PanCap significantly boosts performance across understanding and
generation tasks, offering complementary benefits to large-scale datasets. This
dataset sets a new benchmark for evaluating models on joint panoptic
segmentation and grounded captioning tasks, addressing the need for
high-quality, detailed image-text annotations in multi-modal learning.