Research
He is interested in deep learning and its application for computer vision. He is now working on
image/video inpainting and image synthesis.
He has previous research experience in image/video segmentation, detection and instance
segmentation.
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STransGAN: An Empirical Study on Transformer in GANs
Rui Xu,
Xiangyu Xu,
Kai Chen,
Bolei Zhou,
Chen Change Loy
Technical Report.
[PDF], [Project Page]
In this paper, we conduct a comprehensive empirical study to investigate the intrinsic properties of Transformer in GAN for high-fidelity image synthesis.
Our analysis highlights the importance of feature locality in image generation.
We also examine the influence of residual connections in self-attention layers and propose a novel way to reduce their negative impacts on learning discriminators and conditional generators.
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Positional Encoding as Spatial Inductive Bias in GANs
Rui Xu,
Xintao Wang,
Kai Chen,
Bolei Zhou,
Chen Change Loy
CVPR, 2021
[PDF], [Project Page], [Code]
We investigate the phenomenon of implicit poistional encoding serving as spatial inductive bias in
current GANs. Based on explicit positional encoding, we provide a novel
training strategy, named MS-PIE, to train a single multi-scale GAN model.
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CARAFE: Content-Aware ReAssembly of FEatures
Jiaqi Wang,
Kai Chen,
Rui Xu,
Ziwei Liu,
Chen Change Loy,
Dahua Lin
ICCV, 2019, (Oral)
[PDF]
[Codes]
We have presented Content-Aware ReAssembly of FEatures (CARAFE), a universal, lightweight and
highly effective upsampling operator. It consistently boosts the performances on standard benchmarks
in object detection, instance/semantic segmentation and inpainting.
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