Colorization Transformer swMATH ID: 42457 Software Authors: Manoj Kumar, Dirk Weissenborn, Nal Kalchbrenner Description: Colorization Transformer. We present the Colorization Transformer, a novel approach for diverse high fidelity image colorization based on self-attention. Given a grayscale image, the colorization proceeds in three steps. We first use a conditional autoregressive transformer to produce a low resolution coarse coloring of the grayscale image. Our architecture adopts conditional transformer layers to effectively condition grayscale input. Two subsequent fully parallel networks upsample the coarse colored low resolution image into a finely colored high resolution image. Sampling from the Colorization Transformer produces diverse colorings whose fidelity outperforms the previous state-of-the-art on colorising ImageNet based on FID results and based on a human evaluation in a Mechanical Turk test. Remarkably, in more than 60 Homepage: https://arxiv.org/abs/2102.04432 Source Code: https://github.com/google-research/google-research/tree/master/coltran Dependencies: Python Keywords: Colorization Transformer; Computer Vision; Pattern Recognition; arXiv_cs.CV; Artificial Intelligence; arXiv_cs.AI; Machine Learning; arXiv_cs.LG; Image colorization Related Software: ImageNet; TransGAN; DeblurGAN; BERT; Python; ClusterFit; Flickr30K; PWC-Net; MeshLab; Face2Face; PoseCNN; NIMA; Make3D; EfficientNet; WSABIE; CIDEr; DISN; DVDnet; FaceNet; PointNet Cited in: 1 Publication Standard Articles 1 Publication describing the Software Year Colorization Transformer Manoj Kumar, Dirk Weissenborn, Nal Kalchbrenner 2021 Cited by 1 Author 1 Szeliski, Richard Cited in 1 Serial 1 Texts in Computer Science Cited in 1 Field 1 Computer science (68-XX) Citations by Year