Image semantic segmentation is one of the most important tasks in the field of computer
vision, and it has made great progress in many applications. Many fully supervised deep …

We present SegNeXt, a simple convolutional network architecture for semantic
segmentation. Recent transformer-based models have dominated the field of se-mantic …

Brain-inspired algorithms have become a new trend in next-generation artificial intelligence.
Through research on brain science, the intelligence of remote sensing algorithms can be …

Deep learning approaches have contributed to the rapid development of remote sensing
(RS) image interpretation. The most widely used training paradigm is to use ImageNet …

Deep learning has largely reshaped remote sensing (RS) research for aerial image
understanding and made a great success. Nevertheless, most of the existing deep models …

High spatial resolution (HSR) remote sensing images contain complex foreground-
background relationships, which makes the remote sensing land cover segmentation a …

The trade-off between feature representation power and spatial localization accuracy is
crucial for the dense classification/semantic segmentation of remote sensing images (RSIs) …

The applied research in remote sensing images has been pushed by convolutional neural
network (CNN). Because of the fixed size of the perceptual field, CNN is unable to model …

Reducing the feature distribution shift caused by the factor of visual-environment changes,
named visual-environment changes (VE-changes), is a hot issue in domain adaptation …