Semantic Scholar Open Access 2014 51714 sitasi

Microsoft COCO: Common Objects in Context

Tsung-Yi Lin M. Maire Serge J. Belongie James Hays P. Perona +3 lainnya

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Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (8)

Tsung-Yi Lin

M. Maire

Serge J. Belongie

James Hays

P. Perona

Deva Ramanan

Piotr Dollár

C. L. Zitnick

Format Sitasi

APA MLA BibTeX

Lin, T., Maire, M., Belongie, S.J., Hays, J., Perona, P., Ramanan, D. et al. (2014). Microsoft COCO: Common Objects in Context. https://doi.org/10.1007/978-3-319-10602-1_48

Akses Cepat

Lihat di Sumber doi.org/10.1007/978-3-319-10602-1_48

Informasi Jurnal

Tahun Terbit: 2014
Bahasa: en
Total Sitasi: 51714×
Sumber Database: Semantic Scholar
DOI: 10.1007/978-3-319-10602-1_48
Akses: Open Access ✓