Microsoft COCO: Common objects in context

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

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. Vol. 8693 LNCS PART 5. ed. 2014. p. 740-755 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Lin, TY, Maire, M, Belongie, S, Hays, J, Perona, P, Ramanan, D, Dollár, P & Zitnick, CL 2014, Microsoft COCO: Common objects in context. in Lecture Notes in Computer Science, Springer. PART 5 edn, vol. 8693 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 740-755, 13th European Conference on Computer Vision, ECCV 2014, Zurich, Switzerland, 06/09/2014. https://doi.org/10.1007/978-3-319-10602-1_48

APA

Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014). Microsoft COCO: Common objects in context. In Lecture Notes in Computer Science, Springer (PART 5 ed., Vol. 8693 LNCS, pp. 740-755). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) https://doi.org/10.1007/978-3-319-10602-1_48

Vancouver

Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D et al. Microsoft COCO: Common objects in context. In Lecture Notes in Computer Science, Springer. PART 5 ed. Vol. 8693 LNCS. 2014. p. 740-755. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-10602-1_48

Author

Lin, Tsung Yi ; Maire, Michael ; Belongie, Serge ; Hays, James ; Perona, Pietro ; Ramanan, Deva ; Dollár, Piotr ; Zitnick, C. Lawrence. / Microsoft COCO : Common objects in context. Lecture Notes in Computer Science, Springer. Vol. 8693 LNCS PART 5. ed. 2014. pp. 740-755 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Bibtex

@inproceedings{765ccf5c38c244488bd6191422c2aee0,
title = "Microsoft COCO: Common objects in context",
abstract = "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.",
author = "Lin, {Tsung Yi} and Michael Maire and Serge Belongie and James Hays and Pietro Perona and Deva Ramanan and Piotr Doll{\'a}r and Zitnick, {C. Lawrence}",
year = "2014",
doi = "10.1007/978-3-319-10602-1_48",
language = "English",
volume = "8693 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "740--755",
booktitle = "Lecture Notes in Computer Science, Springer",
edition = "PART 5",
note = "13th European Conference on Computer Vision, ECCV 2014 ; Conference date: 06-09-2014 Through 12-09-2014",

}

RIS

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