most citedGlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Networks

12 citations · 22 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20194 cited

Serif or Sans: Visual Font Analytics on Book Covers and Online Advertisements

Yuto Shinahara, Takuro Karamatsu, Daisuke Harada +2

In this paper, we conduct a large-scale study of font statistics in book covers and online advertisements. Through the statistical study, we try to understand how graphic designers…

cs.CV20195 cited

Modality Conversion of Handwritten Patterns by Cross Variational Autoencoders

Taichi Sumi, Brian Kenji Iwana, Hideaki Hayashi +1

This research attempts to construct a network that can convert online and offline handwritten characters to each other. The proposed network consists of two Variational Auto-Encode…

cs.CV2019

A Trainable Multiplication Layer for Auto-correlation and Co-occurrence Extraction

Hideaki Hayashi, Seiichi Uchida

In this paper, we propose a trainable multiplication layer (TML) for a neural network that can be used to calculate the multiplication between the input features. Taking an image a…

cs.CV201912 cited

GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Networks

Hideaki Hayashi, Kohtaro Abe, Seiichi Uchida

In this paper, we propose GlyphGAN: style-consistent font generation based on generative adversarial networks (GANs). GANs are a framework for learning a generative model using a s…

cs.CV20161 cited

Globally Optimal Object Tracking with Fully Convolutional Networks

Jinho Lee, Brian Kenji Iwana, Shouta Ide +1

Tracking is one of the most important but still difficult tasks in computer vision and pattern recognition. The main difficulties in the tracking field are appearance variation and…