no code implementations • 12 Apr 2024 • Hongtao Wang, Li Long, Jiangshe Zhang, Xiaoli Wei, Chunxia Zhang, Zhenbo Guo
Addressing this, we propose a novel approach using deep graph learning called DGL-FB, constructing a large graph to efficiently extract information.
1 code implementation • 13 Dec 2023 • Xiaojun Xue, Chunxia Zhang, Tianxiang Xu, Zhendong Niu
However, the present few-shot NER models assume that the labeled data are all clean without noise or outliers, and there are few works focusing on the robustness of the cross-domain transfer learning ability to textual adversarial attacks in Few-shot NER.
no code implementations • 9 Jul 2023 • Xiaoli Wei, Chunxia Zhang, Hongtao Wang, Chengli Tan, Deng Xiong, Baisong Jiang, Jiangshe Zhang, Sang-Woon Kim
The model training is established on the denoising diffusion probabilistic model, where U-Net is equipped with the multi-head self-attention to match the noise in each step.
no code implementations • 23 May 2023 • Hongtao Wang, Jiangshe Zhang, Xiaoli Wei, Li Long, Chunxia Zhang
Many deep neural networks (DNNs)-based automatic picking methods have been proposed to accelerate this processing.
no code implementations • 7 Sep 2022 • Hongtao Wang, Jiangshe Zhang, Xiaoli Wei, Chunxia Zhang, Zhenbo Guo, Li Long, Yicheng Wang
Besides, since the gather data is a set of signals which are greatly different from the natural images, it is difficult for the current method to solve the FAT picking problem in case of a low Signal to Noise Ratio (SNR).
1 code implementation • 10 Mar 2021 • Shuang Xu, Jiangshe Zhang, Kai Sun, Zixiang Zhao, Lu Huang, Junmin Liu, Chunxia Zhang
Pansharpening is a fundamental issue in remote sensing field.
1 code implementation • CVPR 2021 • Shuang Xu, Jiangshe Zhang, Zixiang Zhao, Kai Sun, Junmin Liu, Chunxia Zhang
Specifically, two optimization problems regularized by the deep prior are formulated, and they are separately responsible for the generative models for panchromatic images and low resolution multispectral images.
no code implementations • 31 Dec 2020 • Zixiang Zhao, Jiangshe Zhang, Shuang Xu, Kai Sun, Lu Huang, Junmin Liu, Chunxia Zhang
In addition, the latent information of features can be preserved effectively through adversarial training.
1 code implementation • 29 Dec 2020 • Shuang Xu, Lizhen Ji, Zhe Wang, Pengfei Li, Kai Sun, Chunxia Zhang, Jiangshe Zhang
According to the idea that each local region in the fused image should be similar to the sharpest one among source images, this paper presents an optimization-based approach to reduce defocus spread effects.
no code implementations • 2 Sep 2020 • Zixiang Zhao, Jiangshe Zhang, Shuang Xu, Kai Sun, Chunxia Zhang, Junmin Liu
The core idea is that the encoder decomposes an image into base and detail feature maps with low- and high-frequency information, respectively, and that the decoder is responsible for the original image reconstruction.
no code implementations • 12 May 2020 • Zixiang Zhao, Shuang Xu, Jiangshe Zhang, Chengyang Liang, Chunxia Zhang, Junmin Liu
The proposed AUIF model starts with the iterative formulas of two traditional optimization models, which are established to accomplish two-scale decomposition, i. e., separating low-frequency base information and high-frequency detail information from source images.
no code implementations • 12 Feb 2020 • Shuang Xu, Xiaoli Wei, Chunxia Zhang, Junmin Liu, Jiangshe Zhang
It is found that current methods are evaluated on simulated image sets or Lytro dataset.
no code implementations • 26 Apr 2017 • Chunxia Zhang, Yilei Wu, Mu Zhu
In the context of variable selection, ensemble learning has gained increasing interest due to its great potential to improve selection accuracy and to reduce false discovery rate.