Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Warp Consistency for Unsupervised Learning of Dense Correspondences   The key challenge in learning dense correspondences lies in the lack of
ground-truth matches for real image pairs. While photometric consistency losses
provide unsupervised alternatives, they struggle with large appearance changes,
which are ubiquitous in geometric and semantic matching tasks. Moreover,
methods relying on synthetic training pairs often suffer from poor
generalisation to real data.
  We propose Warp Consistency, an unsupervised learning objective for dense
correspondence regression. Our objective is effective even in settings with
large appearance and view-point changes. Given a pair of real images, we first
construct an image triplet by applying a randomly sampled warp to one of the
original images. We derive and analyze all flow-consistency constraints arising
between the triplet. From our observations and empirical results, we design a
general unsupervised objective employing two of the derived constraints. We
validate our warp consistency loss by training three recent dense
correspondence networks for the geometric and semantic matching tasks. Our
approach sets a new state-of-the-art on several challenging benchmarks,
including MegaDepth, RobotCar and TSS. Code and models are at
github.com/PruneTruong/DenseMatching.

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The categories of this paper are: gr-qc
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The categories of this paper are: q-fin
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The categories of this paper are: stat
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
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The categories of this paper are: atom-ph
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The categories of this paper are: nlin
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ph
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The categories of this paper are: econ
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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: plasm-ph