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 non-local 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.