James Hays
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
James Hays has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Antonio Torralba
Massachusetts Institute of Technology
2 shared publications - Aude Oliva
CSAIL, Massachusetts Institute of Technology
2 shared publications - Jianxiong Xiao
Princeton University
2 shared publications - Krista A. Ehinger
Harvard Medical School, Boston, USA
2 shared publications - Aditya Khosla
Massachusetts Institute of Technology, Cambridge, USA
1 shared publication - Alexei A. Efros
University of California
1 shared publication
Latest Publications
- 6 July 2016
- Published by Springer Nature
- p. 59-76
Cross-View Image Geo-localization
- 10 March 2015
- Published by Springer Nature in International Journal of Computer Vision
- Vol. 112 (2) , 131-132
Guest Editorial: Scene Understanding
- 10 March 2015
- Published by Springer Nature in International Journal of Computer Vision
- Vol. 112 (2) , 131-132
Guest Editorial: Scene Understanding
- Top Cited PapersOpen Access
- 13 August 2014
- Published by Springer Nature in International Journal of Computer Vision
- Vol. 119 (1) , 3-22
SUN Database: Exploring a Large Collection of Scene Categories
- 13 August 2014
- Published by Springer Nature in International Journal of Computer Vision
- Vol. 119 (1) , 3-22
Top Cited PapersOpen AccessSUN Database: Exploring a Large Collection of Scene Categories
- Open Access
- 1 January 2013
- Published by Frontiers Media SA in Frontiers in Psychology
- Vol. 4, 506
Basic level scene understanding: categories, attributes and structures
A longstanding goal of computer vision is to build a system that can automatically understand a 3D scene from a single image. This requires extracting semantic concepts and 3D information from 2D images which can depict an enormous variety of environments that comprise our visual world. This paper summarizes our recent efforts toward these goals. First, we describe the richly annotated SUN database which is a collection of annotated images spanning 908 different scene categories with object, attribute, and geometric labels for many scenes. This database allows us to systematically study the space of scenes and to establish a benchmark for scene and object recognition. We augment the categorical SUN database with 102 scene attributes for every image and explore attribute recognition. Finally, we present an integrated system to extract the 3D structure of the scene and objects depicted in an image.
- 1 January 2013
- Published by Frontiers Media SA in Frontiers in Psychology
- Vol. 4, 506
Open AccessBasic level scene understanding: categories, attributes and structures
A longstanding goal of computer vision is to build a system that can automatically understand a 3D scene from a single image. This requires extracting semantic concepts and 3D information from 2D images which can depict an enormous variety of environments that comprise our visual world. This paper summarizes our recent efforts toward these goals. First, we describe the richly annotated SUN database which is a collection of annotated images spanning 908 different scene categories with object, attribute, and geometric labels for many scenes. This database allows us to systematically study the space of scenes and to establish a benchmark for scene and object recognition. We augment the categorical SUN database with 102 scene attributes for every image and explore attribute recognition. Finally, we present an integrated system to extract the 3D structure of the scene and objects depicted in an image.