椰子：現代化 COCO 分割

COCONut: Modernizing COCO Segmentation

April 12, 2024

作者: Xueqing Deng, Qihang Yu, Peng Wang, Xiaohui Shen, Liang-Chieh Chen

cs.AI

摘要

在過去幾十年中，視覺領域見證了顯著的進展，部分歸功於數據集基準的進步。值得注意的是，建立的 COCO 基準推動了現代檢測和分割系統的發展。然而，過去十年來，COCO 分割基準的改進相對緩慢。最初為物件實例配備粗糙的多邊形標註，逐漸納入了用於區域的粗糙超像素標註，隨後根據啟發式方法將其合併以產生全景分割標註。這些標註由不同組的評定者執行，不僅導致粗糙的分割遮罩，還導致分割類型之間的不一致性。在本研究中，我們對 COCO 分割標註進行全面重新評估。通過提高標註質量並擴展數據集，包括超過 5.18M 個全景遮罩的 383K 張圖像，我們引入了 COCONut，即 COCO 下一代通用分割數據集。COCONut 通過精心製作高質量遮罩，在語義、實例和全景分割之間協調分割標註，為所有分割任務建立了堅固的基準。據我們所知，COCONut 是首個大規模通用分割數據集，由人類評定者驗證。我們預計 COCONut 的發布將顯著有助於社群評估新型神經網絡的進展。

English

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.