Abstract
We propose order learning to determine the order graph of classes, representing ranks or priorities, and classify an object instance into one of the classes. To this end, we design a pairwise comparator to categorize the relationship between two instances into one of three cases: one instance is 'greater than,' 'similar to,' or 'smaller than' the other. Then, by comparing an input instance with reference instances and maximizing the consistency among the comparison results, the class of the input can be estimated reliably. We apply order learning to develop a facial age estimator, which provides the state-of-the-art performance. Moreover, the performance is further improved when the order graph is divided into disjoint chains using gender and ethnic group information or even in an unsupervised manner.
| Original language | English |
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| Publication status | Published - 2020 |
| Event | 8th International Conference on Learning Representations, ICLR 2020 - Addis Ababa, Ethiopia Duration: 2020 Apr 30 → … |
Conference
| Conference | 8th International Conference on Learning Representations, ICLR 2020 |
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| Country/Territory | Ethiopia |
| City | Addis Ababa |
| Period | 20/4/30 → … |
Bibliographical note
Funding Information:This work was supported by ‘The Cross-Ministry Giga KOREA Project’ grant funded by the Korea government (MSIT) (No. GK19P0200, Development of 4D reconstruction and dynamic deformable action model based hyperrealistic service technology), and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2018R1A2B3003896).
Publisher Copyright:
© 2020 8th International Conference on Learning Representations, ICLR 2020. All rights reserved.
ASJC Scopus subject areas
- Education
- Linguistics and Language
- Language and Linguistics
- Computer Science Applications