Open Vocabulary Scene Parsing
Name
ICCV_openvoc.pdf
Description
Accepted version
Size
5.49 MB
Format
Adobe PDF
Checksum (MD5)
2039cbfdb1040f7323a4f9b6bb296ee3
Author(s)
Zhao, Hang
Puig Fernandez, Xavier
Zhou, Bolei
Fidler, Sanja
Torralba, Antonio
Date Issued
December 25, 2017
Journal
2017 IEEE International Conference on Computer Vision (ICCV)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Zhao, Hang et al. "Open Vocabulary Scene Parsing." 2017 IEEE International Conference on Computer Vision (ICCV), October 2017, Venice, Italy, Institute of Electrical and Electronics Engineers (IEEE), December 2017 © 2017 IEEE
Version
Author's final manuscript
Abstract
Recognizing arbitrary objects in the wild has been a challenging problem due to the limitations of existing classification models and datasets. In this paper, we propose a new task that aims at parsing scenes with a large and open vocabulary, and several evaluation metrics are explored for this problem. Our approach is a joint image pixel and word concept embeddings framework, where word concepts are connected by semantic relations. We validate the open vocabulary prediction ability of our framework on ADE20K dataset which covers a wide variety of scenes and objects. We further explore the trained joint embedding space to show its interpretability. Keywords: streaming media; vocabulary; training; semantics; predictive models; visualization
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
https://doi.org/10.1109/iccv.2017.221
