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- W4288623670 abstract "Convolutional neural networks (CNNs) have been successfully applied to solve the problem of correspondence estimation between semantically related images. Due to non-availability of large training datasets, existing methods resort to self-supervised or unsupervised training paradigm. In this paper we propose a semi-supervised learning framework that imposes cyclic consistency constraint on unlabeled image pairs. Together with the supervised loss the proposed model achieves state-of-the-art on a benchmark semantic matching dataset." @default.
- W4288623670 created "2022-07-30" @default.
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- W4288623670 date "2019-01-24" @default.
- W4288623670 modified "2023-09-27" @default.
- W4288623670 title "Semi-Supervised Semantic Matching" @default.
- W4288623670 doi "https://doi.org/10.48550/arxiv.1901.08339" @default.
- W4288623670 hasPublicationYear "2019" @default.
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