Microsoft COCO: Common objects in context
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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.
Original language | English |
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Title of host publication | Lecture Notes in Computer Science, Springer |
Number of pages | 16 |
Volume | 8693 LNCS |
Publication date | 2014 |
Edition | PART 5 |
Pages | 740-755 |
DOIs | |
Publication status | Published - 2014 |
Externally published | Yes |
Event | 13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland Duration: 6 Sep 2014 → 12 Sep 2014 |
Conference
Conference | 13th European Conference on Computer Vision, ECCV 2014 |
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Land | Switzerland |
By | Zurich |
Periode | 06/09/2014 → 12/09/2014 |
Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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ISSN | 0302-9743 |
ID: 302817706