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Conference Papers Year : 2022

Super-attention for exemplar-based image colorization

Abstract

In image colorization, exemplar-based methods use a reference color image to guide the colorization of a target grayscale image. In this article, we present a deep learning framework for exemplar-based image colorization which relies on attention layers to capture robust correspondences between high-resolution deep features from pairs of images. To avoid the quadratic scaling problem from classic attention, we rely on a novel attention block computed from superpixel features, which we call super-attention. Super-attention blocks can learn to transfer semantically related color characteristics from a reference image at different scales of a deep network. Our experimental validations highlight the interest of this approach for exemplar-based colorization. We obtain promising results, achieving visually appealing colorization and outperforming state-of-theart methods on different quantitative metrics.
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Dates and versions

hal-03794455 , version 1 (03-10-2022)

Identifiers

  • HAL Id : hal-03794455 , version 1

Cite

Hernan Carrillo, Michaël Clément, Aurélie Bugeau. Super-attention for exemplar-based image colorization. 16th Asian Conference on Computer Vision (ACCV), Dec 2022, Macau, Macau SAR China. ⟨hal-03794455⟩

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