Cross-lingual font style transfer with full-domain convolutional attention

Huihuang Zhao, Tian-le Ji, Paul L. Rosin, Yu-Kun Lai, Weiliang Meng, Yaonan Wang

Abstract: Highlights•We propose a full-domain convolutional attention module to extract information from feature maps.•We propose adaptive fusion to improve the performance of feature fusion.•We design a channel-dependent multi-level feature extraction module to extract style encodings from style feature maps at different scales.•We design a multi-layer perceptual discriminator that more accurately identifies whether the samples are real or not.•We use the quadratic smoothing hinge loss function for the first time in the field of font style transfer.