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Found 5 papers, 0 papers with code
A Spectral Diffusion Prior for Hyperspectral Image Super-Resolution
Motivated by the success of diffusion models, we propose a novel spectral diffusion prior for fusion-based HSI super-resolution.
Explicit Change Relation Learning for Change Detection in VHR Remote Sensing Images
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.
SwinV2DNet: Pyramid and Self-Supervision Compounded Feature Learning for Remote Sensing Images Change Detection
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.
ReAFFPN: Rotation-equivariant Attention Feature Fusion Pyramid Networks for Aerial Object Detection
This paper proposes a Rotation-equivariant Attention Feature Fusion Pyramid Networks for Aerial Object Detection named ReAFFPN.
Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution
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.
