Search Results for author: Shuyang Gu

Found 8 papers, 5 papers with code

StyleSwin: Transformer-based GAN for High-resolution Image Generation

1 code implementation arXiv 2021 BoWen Zhang, Shuyang Gu, Bo Zhang, Jianmin Bao, Dong Chen, Fang Wen, Yong Wang, Baining Guo

To this end, we believe that local attention is crucial to strike the balance between computational efficiency and modeling capacity.

Image Generation

Vector Quantized Diffusion Model for Text-to-Image Synthesis

2 code implementations29 Nov 2021 Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, Baining Guo

Our experiments indicate that the VQ-Diffusion model with the reparameterization is fifteen times faster than traditional AR methods while achieving a better image quality.

 Ranked #1 on Text-to-Image Generation on Oxford 102 Flowers (using extra training data)

Denoising Text to image generation +1

High-Fidelity and Arbitrary Face Editing

no code implementations CVPR 2021 Yue Gao, Fangyun Wei, Jianmin Bao, Shuyang Gu, Dong Chen, Fang Wen, Zhouhui Lian

However, we observe that the generator tends to find a tricky way to hide information from the original image to satisfy the constraint of cycle consistency, making it impossible to maintain the rich details (e. g., wrinkles and moles) of non-editing areas.

Learnable Sampling 3D Convolution for Video Enhancement and Action Recognition

no code implementations22 Nov 2020 Shuyang Gu, Jianmin Bao, Dong Chen

A key challenge in video enhancement and action recognition is to fuse useful information from neighboring frames.

Action Recognition Denoising +3

PriorGAN: Real Data Prior for Generative Adversarial Nets

1 code implementation30 Jun 2020 Shuyang Gu, Jianmin Bao, Dong Chen, Fang Wen

To address these two issues, we propose a novel prior that captures the whole real data distribution for GANs, which are called PriorGANs.

GIQA: Generated Image Quality Assessment

1 code implementation ECCV 2020 Shuyang Gu, Jianmin Bao, Dong Chen, Fang Wen

Generative adversarial networks (GANs) have achieved impressive results today, but not all generated images are perfect.

Image Quality Assessment

Arbitrary Style Transfer with Deep Feature Reshuffle

1 code implementation CVPR 2018 Shuyang Gu, Congliang Chen, Jing Liao, Lu Yuan

We theoretically prove that our new style loss based on reshuffle connects both global and local style losses respectively used by most parametric and non-parametric neural style transfer methods.

Style Transfer

Cannot find the paper you are looking for? You can Submit a new open access paper.