• Author(s) : Xueqing Deng, Qihang Yu, Peng Wang, Xiaohui Shen, Liang-Chieh Chen

In the rapidly evolving field of computer vision, the research community has witnessed remarkable progress in visual recognition tasks, largely driven by advancements in dataset benchmarks like COCO. However, despite its significant contributions, the COCO segmentation benchmark has experienced relatively slow improvement over the past decade.

Originally, the COCO dataset was equipped with coarse polygon annotations for “thing” instances and coarse superpixel annotations for “stuff” regions. These annotations, created by different groups of raters, were later heuristically combined to yield panoptic segmentation annotations. Unfortunately, this approach resulted in coarse segmentation masks and inconsistencies between segmentation types, hindering the development of more accurate and robust segmentation models.
To address these limitations, researchers have undertaken a comprehensive reevaluation of the COCO segmentation annotations. The result is COCONut, the COCO Next Universal segmenTation dataset, a significant advancement in the field of computer vision.

COCONut enhances the annotation quality and expands the dataset to encompass 383,000 images with more than 5.18 million panoptic masks. This new dataset harmonizes segmentation annotations across semantic, instance, and panoptic segmentation, ensuring meticulously crafted high-quality masks. By establishing a robust benchmark for all segmentation tasks, COCONut sets a new standard for universal segmentation datasets.

Notably, COCONut stands as the inaugural large-scale universal segmentation dataset verified by human raters, ensuring the highest level of accuracy and consistency. This rigorous verification process sets COCONut apart from previous datasets and paves the way for more reliable and accurate segmentation models.
The release of COCONut is expected to significantly contribute to the computer vision community’s ability to assess the progress of novel neural networks and advance the state-of-the-art in segmentation tasks. With its comprehensive and high-quality annotations, COCONut provides researchers and developers with a powerful tool to push the boundaries of visual recognition and unlock new possibilities in various applications, such as autonomous vehicles, medical imaging, and robotics.

As the demand for accurate and reliable segmentation models continues to grow, COCONut emerges as a game-changer, offering a robust and meticulously crafted dataset that will undoubtedly accelerate progress in this critical area of computer vision.