COCONut: Modernizing COCO Segmentation
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.
661c8a467a16dc848a269654
Xueqing Deng
admin_assigned
2024-04-18T11:27:55.415Z
65ca9b1743207e438a95e90c
/avatars/8f7bde1c44d8e665a29ee08ce7fedfa4.svg
Xueqing Deng
user
xdeng77
661c8a467a16dc848a269655
Qihang Yu
admin_assigned
2024-04-18T11:28:01.251Z
648010b00b9d0f49849adb19
/avatars/54419bb6e6ee788aba10cd64cd921204.svg
Qihang Yu
user
yucornetto
661c8a467a16dc848a269656
Peng Wang
admin_assigned
2024-04-18T11:28:37.393Z
619c4a5cb392787f0f3ead53
/avatars/e62bc6fed6e9cbe4e0339e050cc3be5f.svg
peng  wang
user
Peng-Wang
661c8a467a16dc848a269657
Xiaohui Shen
admin_assigned
2024-04-18T11:28:44.012Z
6430aa1b32a732121cd81f98
/avatars/5419f8d6d4d36fa5ac83e30667b9fd99.svg
Xiaohui Shen
user
XiaohuiShen
661c8a467a16dc848a269658
Liang-Chieh Chen
COCONut: Modernizing COCO Segmentation
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.
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.