Order Learning Using Partially Ordered Data via Chainization

Seon Ho Lee; Chang Su Kim

doi:10.1007/978-3-031-19778-9_12

Order Learning Using Partially Ordered Data via Chainization

Seon Ho Lee, Chang Su Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

We propose the chainization algorithm for effective order learning when only partially ordered data are available. First, we develop a binary comparator to predict missing ordering relations between instances. Then, by extending the Kahn’s algorithm, we form a chain representing a linear ordering of instances. We fine-tune the comparator over pseudo pairs, which are sampled from the chain, and then re-estimate the linear ordering alternately. As a result, we obtain a more reliable comparator and a more meaningful linear ordering. Experimental results show that the proposed algorithm yields excellent rank estimation performances under various weak supervision scenarios, including semi-supervised learning, domain adaptation, and bipartite cases. The source codes are available at https://github.com/seon92/Chainization.

Original language	English
Title of host publication	Computer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings
Editors	Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	196-211
Number of pages	16
ISBN (Print)	9783031197772
DOIs	https://doi.org/10.1007/978-3-031-19778-9_12
Publication status	Published - 2022
Event	17th European Conference on Computer Vision, ECCV 2022 - Tel Aviv, Israel Duration: 2022 Oct 23 → 2022 Oct 27

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13673 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th European Conference on Computer Vision, ECCV 2022
Country/Territory	Israel
City	Tel Aviv
Period	22/10/23 → 22/10/27

Bibliographical note

Funding Information:
Acknowledgments. This work was supported by the NRF grants funded by the Korea government (MSIT) (No. NRF-2021R1A4A1031864 and No. NRF-2022R1A2B5B03002310) and also by IITP grant funded by the Korea government (MSIT) (No. 2021-0-02068, Artificial Intelligence Innovation Hub).

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Aesthetic assessment
Facial age estimation
Facial expression recognition
Order learning
Rank estimation
Topological sorting

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-19778-9_12

Cite this

Lee, S. H., & Kim, C. S. (2022). Order Learning Using Partially Ordered Data via Chainization. In S. Avidan, G. Brostow, M. Cissé, G. M. Farinella, & T. Hassner (Eds.), Computer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings (pp. 196-211). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13673 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-19778-9_12

Order Learning Using Partially Ordered Data via Chainization. / Lee, Seon Ho; Kim, Chang Su.
Computer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings. ed. / Shai Avidan; Gabriel Brostow; Moustapha Cissé; Giovanni Maria Farinella; Tal Hassner. Springer Science and Business Media Deutschland GmbH, 2022. p. 196-211 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13673 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, SH & Kim, CS 2022, Order Learning Using Partially Ordered Data via Chainization. in S Avidan, G Brostow, M Cissé, GM Farinella & T Hassner (eds), Computer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13673 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 196-211, 17th European Conference on Computer Vision, ECCV 2022, Tel Aviv, Israel, 22/10/23. https://doi.org/10.1007/978-3-031-19778-9_12

Lee SH, Kim CS. Order Learning Using Partially Ordered Data via Chainization. In Avidan S, Brostow G, Cissé M, Farinella GM, Hassner T, editors, Computer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 196-211. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-19778-9_12

Lee, Seon Ho ; Kim, Chang Su. / Order Learning Using Partially Ordered Data via Chainization. Computer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings. editor / Shai Avidan ; Gabriel Brostow ; Moustapha Cissé ; Giovanni Maria Farinella ; Tal Hassner. Springer Science and Business Media Deutschland GmbH, 2022. pp. 196-211 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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