Scene parsing through ADE20K dataset
Name
scene-parse-camera-ready.pdf
Description
Accepted version
Size
4.65 MB
Format
Adobe PDF
Checksum (MD5)
4341ca92f6e069e5436c0be68c02d76e
Author(s)
Zhou, Bolei
Zhao, Hang
Puig Fernandez, Francesco Xavier
Fidler, Sanja
Barriuso, Adela
Torralba, Antonio
Date Issued
2017
Journal
Proceedings, 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Zhou, Bolei, et al., "Scene parsing through ADE20K dataset." Proceedings, 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Piscataway, N.J.: IEEE, 2017): p. 5122-30 doi 10.1109/CVPR.2017.544 ©2017 Author(s)
Version
Author's final manuscript
Abstract
Scene parsing, or recognizing and segmenting objects and stuff in an image, is one of the key problems in computer vision. Despite the community's efforts in data collection, there are still few image datasets covering a wide range of scenes and object categories with dense and detailed annotations for scene parsing. In this paper, we introduce and analyze the ADE20K dataset, spanning diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts. A scene parsing benchmark is built upon the ADE20K with 150 object and stuff classes included. Several segmentation baseline models are evaluated on the benchmark. A novel network design called Cascade Segmentation Module is proposed to parse a scene into stuff, objects, and object parts in a cascade and improve over the baselines. We further show that the trained scene parsing networks can lead to applications such as image content removal and scene synthesis. ©2017 Paper presented at the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), July 21-26, 2017, Honolulu, Hawaii.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1109/CVPR.2017.544
