AWhat changed
Designed a large-vocabulary instance-segmentation dataset and per-category positive/negative evaluation subsets, enabling long-tail object recognition without exhaustive labeling of every image-category pair.
Scientific scope
Designed a large-vocabulary instance-segmentation dataset and per-category positive/negative evaluation subsets, enabling long-tail object recognition without exhaustive labeling of every image-category pair. Originalv1 describes ongoing collection: approximately2million masks/164k images are projected targets, not certified completed release counts. Federated evaluation here means category-wise annotation subsets, not federated distributed model training. Incompletely annotated positive images have special false-positive handling; ordinary dense COCO AP cannot be applied indiscriminately.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Originalv1 describes ongoing collection: approximately2million masks/164k images are projected targets, not certified completed release counts. Federated evaluation here means category-wise annotation subsets, not federated distributed model training. Incompletely annotated positive images have special false-positive handling; ordinary dense COCO AP cannot be applied indiscriminately.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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