no code implementations • 15 Nov 2023 • Jianjun Liu, Zebin Wu, Liang Xiao
Motivated by the success of diffusion models, we propose a novel spectral diffusion prior for fusion-based HSI super-resolution.
no code implementations • 14 Nov 2023 • Dalong Zheng, Zebin Wu, Jia Liu, Chih-Cheng Hung, Zhihui Wei
In order to fully mine these three kinds of change features, we propose the triple branch network combining the transformer and convolutional neural network (CNN) to extract and fuse these change features from two perspectives of global information and local information, respectively.
no code implementations • 22 Aug 2023 • Dalong Zheng, Zebin Wu, Jia Liu, Zhihui Wei
Therefore, based on swin transformer V2 (Swin V2) and VGG16, we propose an end-to-end compounded dense network SwinV2DNet to inherit the advantages of both transformer and CNN and overcome the shortcomings of existing networks in feature learning.
no code implementations • 17 Oct 2022 • Chongyu Sun, Yang Xu, Zebin Wu, Zhihui Wei
This paper proposes a Rotation-equivariant Attention Feature Fusion Pyramid Networks for Aerial Object Detection named ReAFFPN.
no code implementations • 22 Oct 2021 • Jianjun Liu, Zebin Wu, Liang Xiao, Xiao-Jun Wu
Inspired by the specific properties of model, we make the first attempt to design a model inspired deep network for HSI super-resolution in an unsupervised manner.