80 citations · 121 across the 9 of their papers we have counts for
9 papers
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
Theory of periodic convolutional neural network
Yuqing Liu
We introduce a novel convolutional neural network architecture, termed the \emph{periodic CNN}, which incorporates periodic boundary conditions into the convolutional layers. Our m…
ISTA-Inspired Network for Image Super-Resolution
Yuqing Liu, Wei Zhang, Weifeng Sun +3
Deep learning for image super-resolution (SR) has been investigated by numerous researchers in recent years. Most of the works concentrate on effective block designs and improve th…
Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution
Yuqing Liu, Qi Jia, Jian Zhang +4
As a highly ill-posed issue, single image super-resolution (SISR) has been widely investigated in recent years. The main task of SISR is to recover the information loss caused by t…
Textural-Perceptual Joint Learning for No-Reference Super-Resolution Image Quality Assessment
Yuqing Liu, Qi Jia, Shanshe Wang +2
Image super-resolution (SR) has been widely investigated in recent years. However, it is challenging to fairly estimate the performance of various SR methods, as the lack of reliab…
Learning Weighting Map for Bit-Depth Expansion within a Rational Range
Yuqing Liu, Qi Jia, Jian Zhang +4
Bit-depth expansion (BDE) is one of the emerging technologies to display high bit-depth (HBD) image from low bit-depth (LBD) source. Existing BDE methods have no unified solution f…
Cross-SRN: Structure-Preserving Super-Resolution Network with Cross Convolution
Yuqing Liu, Qi Jia, Xin Fan +3
It is challenging to restore low-resolution (LR) images to super-resolution (SR) images with correct and clear details. Existing deep learning works almost neglect the inherent str…
Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution
Yuqing Liu, Xinfeng Zhang, Shanshe Wang +2
Multi-scale design has been considered in recent image super-resolution (SR) works to explore the hierarchical feature information. Existing multi-scale networks aim to build elabo…
Iterative Network for Image Super-Resolution
Yuqing Liu, Shiqi Wang, Jian Zhang +3
Single image super-resolution (SISR), as a traditional ill-conditioned inverse problem, has been greatly revitalized by the recent development of convolutional neural networks (CNN…