Learning Ordinal Relationships for Mid-Level Vision
We propose a framework that infers mid-level visual properties of an image by learning about ordinal relationships. Instead of estimating metric quantities directly, the system proposes pairwise relationship estimates for points in the input image. These sparse probabilistic ordinal measurements are...
Main Authors: | Krishnan, Dilip, Freeman, William T., Zoran, Daniel, Isola, Phillip John |
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Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
Format: | Article |
Language: | en_US |
Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2018
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Online Access: | http://hdl.handle.net/1721.1/116143 https://orcid.org/0000-0003-4988-9771 https://orcid.org/0000-0002-1411-6704 |
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