mlsanityData to 14 Oct 2026

Microsoft COCO: Common Objects in Context

arXiv 1405.0312 ↗First posted 1 May 2014cs.CVcited by 17,608

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Cited by 1,896 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.

Key facts. Cited by 17,608 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 May 2014.

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Citing papers per year: 2018: 1; 2019: 3; 2020: 4; 2021: 2289; 2022: 2748; 2023: 3458; 2024: 3569; 2025: 3167; 2026: 2369.

17,608 papers.

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