LabelMe

LabelMe is a project created by the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) which provides a dataset of digital images… (More)
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Topic mentions per year

Topic mentions per year

2005-2017
051020052017

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2010
Review
2010
Central to the development of computer vision systems is the collection and use of annotated images spanning our visual world… (More)
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Highly Cited
2010
Highly Cited
2010
The Bag-of-Words (BoW) model is a promising image representation technique for image categorization and annotation tasks. One… (More)
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Highly Cited
2010
Highly Cited
2010
Occlusion reasoning is a fundamental problem in computer vision. In this paper, we propose an algorithm to recover the occlusion… (More)
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Highly Cited
2009
Highly Cited
2009
Currently, video analysis algorithms suffer from lack of information regarding the objects present, their interactions, as well… (More)
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Highly Cited
2008
Highly Cited
2008
The Internet contains billions of images, freely available online. Methods for efficiently searching this incredibly rich… (More)
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Highly Cited
2008
Highly Cited
2008
We pose the recognition problem as data association. In this setting, a novel object is explained solely in terms of a small set… (More)
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Highly Cited
2007
Highly Cited
2007
We seek to build a large collection of images with ground truth labels to be used for object detection and recognition research… (More)
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Highly Cited
2007
Highly Cited
2007
The explosion of the Internet provides us with a tremendous resource of images shared online. It also confronts vision… (More)
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Highly Cited
2006
Highly Cited
2006
Given a large dataset of images, we seek to automatically determine the visually similar object and scene classes together with… (More)
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2005
2005
Research in object detection and recognition in cluttered scenes requires large image collections with ground truth labels. The… (More)
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