AWhat changed
Created a crowd-annotation pipeline and dataset of non-iconic everyday scenes with object categories and per-instance segmentations to study contextual recognition and localization.
Scientific scope
Created a crowd-annotation pipeline and dataset of non-iconic everyday scenes with object categories and per-instance segmentations to study contextual recognition and localization. Original2014v1 describes91categories,328Kimages and2.5Minstances; these figures are not later standard80-category training/evaluation split counts. Inherits earlier datasets and hierarchical annotation tools. Dataset contribution is distinct from later detection/segmentation algorithms; no dataset pixels, crowd annotations or baseline model results replayed.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Original2014v1 describes91categories,328Kimages and2.5Minstances; these figures are not later standard80-category training/evaluation split counts. Inherits earlier datasets and hierarchical annotation tools. Dataset contribution is distinct from later detection/segmentation algorithms; no dataset pixels, crowd annotations or baseline model results replayed.
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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