DCTM

Discrete-Continuous Transformation Matching for Semantic Flow

Seungryong Kim, Dongbo Min, Stephen Lin, Kwanghoon Sohn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

Techniques for dense semantic correspondence have provided limited ability to deal with the geometric variations that commonly exist between semantically similar images. While variations due to scale and rotation have been examined, there is a lack of practical solutions for more complex deformations such as affine transformations because of the tremendous size of the associated solution space. To address this problem, we present a discrete-continuous transformation matching (DCTM) framework where dense affine transformation fields are inferred through a discrete label optimization in which the labels are iteratively updated via continuous regularization. In this way, our approach draws solutions from the continuous space of affine transformations in a manner that can be computed efficiently through constant-time edge-aware filtering and a proposed affine-varying CNN-based descriptor. Experimental results show that this model outperforms the state-of-the-art methods for dense semantic correspondence on various benchmarks.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4539-4548
Number of pages10
ISBN (Electronic)9781538610329
DOIs
Publication statusPublished - 2017 Dec 22
Event16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy
Duration: 2017 Oct 222017 Oct 29

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2017-October
ISSN (Print)1550-5499

Other

Other16th IEEE International Conference on Computer Vision, ICCV 2017
CountryItaly
CityVenice
Period17/10/2217/10/29

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Semantics
Labels

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Kim, S., Min, D., Lin, S., & Sohn, K. (2017). DCTM: Discrete-Continuous Transformation Matching for Semantic Flow. In Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017 (pp. 4539-4548). [8237747] (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2017-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCV.2017.485
Kim, Seungryong ; Min, Dongbo ; Lin, Stephen ; Sohn, Kwanghoon. / DCTM : Discrete-Continuous Transformation Matching for Semantic Flow. Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 4539-4548 (Proceedings of the IEEE International Conference on Computer Vision).
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Kim, S, Min, D, Lin, S & Sohn, K 2017, DCTM: Discrete-Continuous Transformation Matching for Semantic Flow. in Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017., 8237747, Proceedings of the IEEE International Conference on Computer Vision, vol. 2017-October, Institute of Electrical and Electronics Engineers Inc., pp. 4539-4548, 16th IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, 17/10/22. https://doi.org/10.1109/ICCV.2017.485

DCTM : Discrete-Continuous Transformation Matching for Semantic Flow. / Kim, Seungryong; Min, Dongbo; Lin, Stephen; Sohn, Kwanghoon.

Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 4539-4548 8237747 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2017-October).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Kim S, Min D, Lin S, Sohn K. DCTM: Discrete-Continuous Transformation Matching for Semantic Flow. In Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 4539-4548. 8237747. (Proceedings of the IEEE International Conference on Computer Vision). https://doi.org/10.1109/ICCV.2017.485