no code implementations • 17 Jun 2023 • Weichen Zhang, Xiang Zhou, Yukang Cao, Wensen Feng, Chun Yuan
We improve from NeRF and propose a novel framework that, by leveraging the parametric 3DMM models, can reconstruct a high-fidelity drivable face avatar and successfully handle the unseen expressions.
no code implementations • 28 May 2022 • Jinli Liao, Yikang Ding, Yoli Shavit, Dihe Huang, Shihao Ren, Jia Guo, Wensen Feng, Kai Zhang
In this work, we propose Window-based Transformers (WT) for local feature matching and global feature aggregation in multi-view stereo.
no code implementations • 8 May 2022 • Wenxuan Fang, Kai Zhang, Yoli Shavit, Wensen Feng
Our method learns local and global augmentation policies which will increase the training loss, while the image retrieval network is forced to learn more powerful features for discriminating increasingly difficult examples.
no code implementations • CVPR 2022 • Yan Shi, Jun-Xiong Cai, Yoli Shavit, Tai-Jiang Mu, Wensen Feng, Kai Zhang
Graph Neural Networks (GNNs) with attention have been successfully applied for learning visual feature matching.
1 code implementation • 9 Oct 2020 • Weihao Xia, Yujiu Yang, Jing-Hao Xue, Wensen Feng
The encoder maps images into a well-disentangled and hierarchically-organized latent space.
no code implementations • 17 Jul 2018 • Peng Qiao, Yong Dou, Yunjin Chen, Wensen Feng
On the contrary, the regularization term learned via discriminative approaches are usually trained for a specific image restoration problem, and fail in the problem for which it is not trained.
no code implementations • 24 Feb 2017 • Wensen Feng, Yunjin Chen
Therefore, in this study we aim to propose an efficient despeckling model with both high computational efficiency and high recovery quality.
no code implementations • 24 Feb 2017 • Peng Qiao, Yong Dou, Wensen Feng, Yunjin Chen
In order to preserve the expected property that end-to-end training is available, we exploit the NSS prior by a set of non-local filters, and derive our proposed trainable non-local reaction diffusion (TNLRD) model for image denoising.
no code implementations • 21 Sep 2016 • Wensen Feng, Peng Qiao, Xuanyang Xi, Yunjin Chen
However, in recent two years, discriminatively trained local approaches have started to outperform previous non-local models and have been attracting increasing attentions due to the additional advantage of computational efficiency.
no code implementations • 19 Sep 2016 • Wensen Feng, Hong Qiao, Yunjin Chen
We start with a direct modeling in the original image domain by taking into account the Poisson noise statistics, which performs generally well for the cases of high SNR.
no code implementations • 10 Oct 2015 • Wensen Feng, Yunjin Chen
The degradation of the acquired signal by Poisson noise is a common problem for various imaging applications, such as medical imaging, night vision and microscopy.
no code implementations • 21 Apr 2014 • Yunjin Chen, Wensen Feng, René Ranftl, Hong Qiao, Thomas Pock
The Fields of Experts (FoE) image prior model, a filter-based higher-order Markov Random Fields (MRF) model, has been shown to be effective for many image restoration problems.