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In this paper, we present a fast exemplar-based image colorization approachusing color embeddings named Color2Embed. Generally, due to the difficulty ofobtaining input and ground truth image pairs, it is hard to train aexemplar-based colorization model with unsupervised and unpaired trainingmanner. Current algorithms usually strive to achieve two procedures: i)retrieving a large number of reference images with high similarity forpreparing training dataset, which is inevitably time-consuming and tedious; ii)designing complicated modules to transfer the colors of the reference image tothe target image, by calculating and leveraging the deep semanticcorrespondence between them (e.g., non-local operation), which iscomputationally expensive during testing. Contrary to the previous methods, weadopt a self-augmented self-reference learning scheme, where the referenceimage is generated by graphical transformations from the original colorful onewhereby the training can be formulated in a paired manner. Second, in order toreduce the process time, our method explicitly extracts the color embeddingsand exploits a progressive style feature Transformation network, which injectsthe color embeddings into the reconstruction of the final image. Such design ismuch more lightweight and intelligible, achieving appealing performance withfast processing speed.

Color2Embed: Fast Exemplar-Based Image Colorization using Color Embeddings