most citedAwesome Typography: Statistics-Based Text Effects Transfer

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

collaborators

5 papers

cs.CV20231 cited

Scenimefy: Learning to Craft Anime Scene via Semi-Supervised Image-to-Image Translation

Yuxin Jiang, Liming Jiang, Shuai Yang +1

Automatic high-quality rendering of anime scenes from complex real-world images is of significant practical value. The challenges of this task lie in the complexity of the scenes,…

cs.CV2023

GP-UNIT: Generative Prior for Versatile Unsupervised Image-to-Image Translation

Shuai Yang, Liming Jiang, Ziwei Liu +1

Recent advances in deep learning have witnessed many successful unsupervised image-to-image translation models that learn correspondences between two visual domains without paired…

cs.LG2023

Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommender

Shuai Yang, Lixin Zhang, Feng Xia +1

There are roughly three stages in real industrial recommendation systems, candidates generation (retrieval), ranking and reranking. Individual-level diversity and system-level dive…

cs.LG20232 cited

Learning to Rank Normalized Entropy Curves with Differentiable Window Transformation

Hanyang Liu, Shuai Yang, Feng Qi +1

Recent automated machine learning systems often use learning curves ranking models to inform decisions about when to stop unpromising trials and identify better model configuration…

cs.CV20162 cited

Awesome Typography: Statistics-Based Text Effects Transfer

Shuai Yang, Jiaying Liu, Zhouhui Lian +1

In this work, we explore the problem of generating fantastic special-effects for the typography. It is quite challenging due to the model diversities to illustrate varied text effe…