J
James Hays
Researcher at Georgia Institute of Technology
Publications - 117
Citations - 49347
James Hays is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Object detection & Image retrieval. The author has an hindex of 49, co-authored 111 publications receiving 33710 citations. Previous affiliations of James Hays include Brown University & Facebook.
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Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: 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 by gathering images of complex everyday scenes containing common objects in their natural context.
Proceedings ArticleDOI
SUN database: Large-scale scene recognition from abbey to zoo
TL;DR: This paper proposes the extensive Scene UNderstanding (SUN) database that contains 899 categories and 130,519 images and uses 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition and establish new bounds of performance.
Proceedings ArticleDOI
Scene completion using millions of photographs
James Hays,Alexei A. Efros +1 more
TL;DR: A new image completion algorithm powered by a huge database of photographs gathered from the Web, requiring no annotations or labelling by the user, that can generate a diverse set of results for each input image and allow users to select among them.
Proceedings ArticleDOI
IM2GPS: estimating geographic information from a single image
James Hays,Alexei A. Efros +1 more
TL;DR: This paper proposes a simple algorithm for estimating a distribution over geographic locations from a single image using a purely data-driven scene matching approach and shows that geolocation estimates can provide the basis for numerous other image understanding tasks such as population density estimation, land cover estimation or urban/rural classification.
Proceedings ArticleDOI
Argoverse: 3D Tracking and Forecasting With Rich Maps
Ming-Fang Chang,Deva Ramanan,James Hays,John Lambert,Patsorn Sangkloy,Jasvinder A. Singh,Slawomir Bak,Andrew Hartnett,De Wang,Peter W. Carr,Simon Lucey +10 more
TL;DR: Argoverse includes sensor data collected by a fleet of autonomous vehicles in Pittsburgh and Miami as well as 3D tracking annotations, 300k extracted interesting vehicle trajectories, and rich semantic maps, which contain rich geometric and semantic metadata which are not currently available in any public dataset.