2014 MicrosoftCOCOCommonObjectsinCon
- (Lin et al., 2014) ⇒ Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar, and C. Lawrence Zitnick. (2014). “Microsoft COCO: Common Objects in Context.” In: Proceeding of the 13th European Conference in Computer Vision Part V (ECCV 2014).
Subject Headings: MS COCO Dataset; COCO Object Detection Task; Joint COCO And LVIS Recognition Challenge Workshop At ECCV 2020, Deformable Parts Model.
Notes
Cited By
- Google Scholar: ~ 12,892 Citations, Retrieved: 2020-12-12.
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Author Keywords
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 result]]s using a Deformable Parts Model.
References
BibTeX
@inproceedings{2014_MicrosoftCOCOCommonObjectsinCon,
author = {Tsung-Yi Lin and
Michael Maire and
Serge J. Belongie and
James Hays and
Pietro Perona and
Deva Ramanan and
Piotr Dollar and
C. Lawrence Zitnick},
editor = {David J. Fleet and
Tomas Pajdla and
Bernt Schiele and
Tinne Tuytelaars},
title = {Microsoft COCO: Common Objects in Context},
booktitle = {Proceeding of the 13th European Conference in Computer Vision Part V (ECCV 2014)},
series = {Lecture Notes in Computer Science},
volume = {8693},
pages = {740--755},
publisher = {Springer},
year = {2014},
address = {Zurich, Switzerland},
date = {September 6-12, 2014},
url = {https://doi.org/10.1007/978-3-319-10602-1\_48},
doi = {10.1007/978-3-319-10602-1\_48},
}