title: COCONut: Modernizing COCO Segmentation
summary: 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.
paper_authors._id: 661c8a467a16dc848a269654
paper_authors.name: Xueqing Deng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-18T11:27:55.415Z
paper_authors._id: 65ca9b1743207e438a95e90c
paper_authors.avatarUrl: /avatars/8f7bde1c44d8e665a29ee08ce7fedfa4.svg
paper_authors.fullname: Xueqing Deng
paper_authors.type: user
paper_authors.user: xdeng77
paper_authors._id: 661c8a467a16dc848a269655
paper_authors.name: Qihang Yu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-18T11:28:01.251Z
paper_authors._id: 648010b00b9d0f49849adb19
paper_authors.avatarUrl: /avatars/54419bb6e6ee788aba10cd64cd921204.svg
paper_authors.fullname: Qihang Yu
paper_authors.type: user
paper_authors.user: yucornetto
paper_authors._id: 661c8a467a16dc848a269656
paper_authors.name: Peng Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-18T11:28:37.393Z
paper_authors._id: 619c4a5cb392787f0f3ead53
paper_authors.avatarUrl: /avatars/e62bc6fed6e9cbe4e0339e050cc3be5f.svg
paper_authors.fullname: peng  wang
paper_authors.type: user
paper_authors.user: Peng-Wang
paper_authors._id: 661c8a467a16dc848a269657
paper_authors.name: Xiaohui Shen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-18T11:28:44.012Z
paper_authors._id: 6430aa1b32a732121cd81f98
paper_authors.avatarUrl: /avatars/5419f8d6d4d36fa5ac83e30667b9fd99.svg
paper_authors.fullname: Xiaohui Shen
paper_authors.type: user
paper_authors.user: XiaohuiShen
paper_authors._id: 661c8a467a16dc848a269658
paper_authors.name: Liang-Chieh Chen
paper_title: COCONut: Modernizing COCO Segmentation
paper_summary: 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.
paper_ai_summary: COCONut, a reevaluated and expanded version of the COCO dataset, provides high-quality annotations for semantic, instance, and panoptic segmentation tasks, serving as the first large-scale universal segmentation benchmark.