Microsoft COCO: Common objects in context
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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Microsoft COCO : Common objects in context. / Lin, Tsung Yi; Maire, Michael; Belongie, Serge; Hays, James; Perona, Pietro; Ramanan, Deva; Dollár, Piotr; Zitnick, C. Lawrence.
Lecture Notes in Computer Science, Springer. Bind 8693 LNCS PART 5. udg. 2014. s. 740-755 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Microsoft COCO
T2 - 13th European Conference on Computer Vision, ECCV 2014
AU - Lin, Tsung Yi
AU - Maire, Michael
AU - Belongie, Serge
AU - Hays, James
AU - Perona, Pietro
AU - Ramanan, Deva
AU - Dollár, Piotr
AU - Zitnick, C. Lawrence
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84906493406&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10602-1_48
DO - 10.1007/978-3-319-10602-1_48
M3 - Article in proceedings
AN - SCOPUS:84906493406
VL - 8693 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 740
EP - 755
BT - Lecture Notes in Computer Science, Springer
Y2 - 6 September 2014 through 12 September 2014
ER -
ID: 302817706