In recent decades, the vision community has witnessed remarkable progress in
visual recognition, partially owing to advancements in dataset benchmarks.
Notably, the established COCO benchmark has propelled the development of modern
detection and segmentation systems. However, the COCO segmentation benchmark
has seen comparatively slow improvement over the last decade. Originally
equipped with coarse polygon annotations for thing instances, it gradually
incorporated coarse superpixel annotations for stuff regions, which were
subsequently heuristically amalgamated to yield panoptic segmentation
annotations. These annotations, executed by different groups of raters, have
resulted not only in coarse segmentation masks but also in inconsistencies
between segmentation types. In this study, we undertake a comprehensive
reevaluation of the COCO segmentation annotations. By enhancing the annotation
quality and expanding the dataset to encompass 383K images with more than 5.18M
panoptic masks, we introduce COCONut, the COCO Next Universal segmenTation
dataset. COCONut harmonizes segmentation annotations across semantic, instance,
and panoptic segmentation with meticulously crafted high-quality masks, and
establishes a robust benchmark for all segmentation tasks. To our knowledge,
COCONut stands as the inaugural large-scale universal segmentation dataset,
verified by human raters. We anticipate that the release of COCONut will
significantly contribute to the community's ability to assess the progress of
novel neural networks.

最近几十年来，视觉学界在视觉识别方面取得了显著进展，其中一部分要归功于数据集基准的进步。本研究通过提高标注质量和扩大数据集范围，引入了 COCONut 数据集，该数据集包含 383K 张图像和超过 5.18M 个分割掩模，并通过特别精心设计的高质量掩模将语义、实例和全景分割的标注统一起来，为所有分割任务建立了一个稳健的基准。我们相信 COCONut 的发布将极大地促进社区评估新型神经网络的进展。

COCONut：现代化 COCO 分割

COCONut: Modernizing COCO Segmentation

Performing data augmentation for learning deep neural networks is well known
to be important for training visual recognition systems. By artificially
increasing the number of training examples, it helps reducing overfitting and
improves generalization. For object detection, classical approaches for data
augmentation consist of generating images obtained by basic geometrical
transformations and color changes of original training images. In this work, we
go one step further and leverage segmentation annotations to increase the
number of object instances present on training data. For this approach to be
successful, we show that modeling appropriately the visual context surrounding
objects is crucial to place them in the right environment. Otherwise, we show
that the previous strategy actually hurts. With our context model, we achieve
significant mean average precision improvements when few labeled examples are
available on the VOC'12 benchmark.

本研究旨在通过利用分割注释来增加训练数据中的物体实例数量，通过适当地对物体周围的视觉环境进行建模以在物体所处的正确环境中放置它们，以提高少量标记样本的 VOC'12 基准测试平均精度。