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Microsoft COCO: Common Objects in Context

Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Dollár, Piotr and Zitnick, C. Lawrence (2014) Microsoft COCO: Common Objects in Context. In: Computer Vision – ECCV 2014. Lecture Notes in Computer Science. Vol.V. No.8693. Springer , Cham, Switzerland, pp. 740-755. ISBN 9783319106014.

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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.

Item Type:Book Section
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URLURL TypeDescription Paper ReadCube access
Belongie, Serge0000-0002-0388-5217
Perona, Pietro0000-0002-7583-5809
Additional Information:© 2014 Springer International Publishing Switzerland. Funding for all crowd worker tasks was provided by Microsoft. P.P. and D.R. were supported by ONR MURI Grant N00014-10-1-0933. We would like to thank all members of the community who provided valuable feedback throughout the process of defining and collecting the dataset.
Funding AgencyGrant Number
Office of Naval Research (ONR)N00014-10-1-0933
Subject Keywords:Object Detection; Common Object; Object Category; Object Instance; Scene Understanding
Series Name:Lecture Notes in Computer Science
Issue or Number:8693
Record Number:CaltechAUTHORS:20190327-124700504
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94215
Deposited By: Tony Diaz
Deposited On:27 Mar 2019 20:26
Last Modified:26 May 2020 16:51

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