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- W4288623398 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)cite{proposal_flow}. 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." @default.
- W4288623398 created "2022-07-30" @default.
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- W4288623398 date "2019-01-24" @default.
- W4288623398 modified "2023-10-16" @default.
- W4288623398 title "Semantic Matching by Weakly Supervised 2D Point Set Registration" @default.
- W4288623398 doi "https://doi.org/10.48550/arxiv.1901.08341" @default.
- W4288623398 hasPublicationYear "2019" @default.
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