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Conference Papers Year : 2022

Non-local matching of superpixel-based deep features for color transfer

Abstract

In this article, we propose a new method for matching high-resolution feature maps from CNNs using attention mechanisms. To avoid the quadratic scaling problem of all-to-all attention, this method relies on a superpixel-based pooling dimensionality reduction strategy. From this pooling, we efficiently compute nonlocal similarities between pairs of images. To illustrate the interest of these new methodological blocks, we apply them to the problem of color transfer between a target image and a reference image. While previous methods for this application can suffer from poor spatial and color coherence, our approach tackles these problems by leveraging on a robust non-local matching between high-resolution low-level features. Finally, we highlight the interest in this approach by showing promising results in comparison with state-of-the-art methods.
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Dates and versions

hal-03442598 , version 1 (23-11-2021)

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  • HAL Id : hal-03442598 , version 1

Cite

Hernan Carrillo, Michaël Clément, Aurélie Bugeau. Non-local matching of superpixel-based deep features for color transfer. International Conference on Computer Vision Theory and Applications, Feb 2022, Vienne (on line), Austria. ⟨hal-03442598⟩

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