Semantic Understanding of Scenes Through the ADE20K Dataset
Name
1608.05442.pdf
Description
Submitted version
Size
7.64 MB
Format
Adobe PDF
Checksum (MD5)
4b15d31c7791cd5c146cd5397b7af60b
Author(s)
Zhou, Bolei
Zhao, Hang
Puig Fernandez, Xavier
Xiao, Tete
Fidler, Sanja
Barriuso, Adela
Torralba, Antonio
Date Issued
December 2018
Journal
International Journal of Computer Vision
Publisher
Springer Nature
Citation
Zhou, Bolei, et al. "Semantic Understanding of Scenes Through the ADE20K Dataset." International Journal of Computer Vision 127 (2019): 302–321. https://doi.org/10.1007/s11263-018-1140-0 © 2018 Author(s)
Version
Original manuscript
Abstract
Semantic understanding of visual scenes is one of the holy grails of computer vision. Despite efforts of the community in data collection, there are still few image datasets covering a wide range of scenes and object categories with pixel-wise annotations for scene understanding. In this work, we present a densely annotated dataset ADE20K, which spans diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts. Totally there are 25k images of the complex everyday scenes containing a variety of objects in their natural spatial context. On average there are 19.5 instances and 10.5 object classes per image. Based on ADE20K, we construct benchmarks for scene parsing and instance segmentation. We provide baseline performances on both of the benchmarks and re-implement state-of-the-art models for open source. We further evaluate the effect of synchronized batch normalization and find that a reasonably large batch size is crucial for the semantic segmentation performance. We show that the networks trained on ADE20K are able to segment a wide variety of scenes and objects.
MIT Department
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
https://doi.org/10.1007/S11263-018-1140-0
