Semantic matching by weakly supervised 2D point set registration

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Abstract

In this paper we address the problem of establishing correspondences between different instances of the same object. The problem is posed as finding the geometric transformation that aligns a given image pair. We use a convolutional neural network (CNN) to directly regress the parameters of the transformation model. The alignment problem is defined in the setting where an unordered set of semantic key-points per image are available, but, without the correspondence information. To this end we propose a novel loss function based on cyclic consistency that solves this 2D point set registration problem by inferring the optimal geometric transformation model parameters. We train and test our approach on a standard benchmark dataset Proposal-Flow (PF-PASCAL)[8]. The proposed approach achieves state-of-the-art results demonstrating the effectiveness of the method. In addition, we show our approach further benefits from additional training samples in PF-PASCAL generated by using category level information.

Original languageEnglish
Title of host publication2019 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV)
PublisherIEEE
Pages1061-1069
Number of pages9
ISBN (Electronic)9781728119755
DOIs
Publication statusPublished - 4 Mar 2019
MoE publication typeA4 Article in a conference publication
EventIEEE Winter Conference on Applications of Computer Vision - Waikoloa Village, United States
Duration: 7 Jan 201911 Jan 2019
Conference number: 19

Conference

ConferenceIEEE Winter Conference on Applications of Computer Vision
Abbreviated titleWACV
CountryUnited States
CityWaikoloa Village
Period07/01/201911/01/2019

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  • Cite this

    Laskar, Z., Tavakoli, H. R., & Kannala, J. (2019). Semantic matching by weakly supervised 2D point set registration. In 2019 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV) (pp. 1061-1069). [8658796] IEEE. https://doi.org/10.1109/WACV.2019.00118