Abstract: Graph Neural Networks (GNNs) are rapidly becoming essential tools in deep learning, but their effectiveness when applied to images is often limited by challenges in graph representation.
Abstract: Land cover (LC) classification remains challenging, even with advanced machine learning (ML) techniques. Pixel-wise convolutional neural networks (CNNs) are currently widely used for image ...
Deep learning has been successfully applied in the field of medical diagnosis, and improving the accurate classification of ...
Abstract: Fine-grained image classification (FGIC) remains a challenging task due to subtle inter-class differences and significant intra-class variations, particularly under limited training data.
President Donald Trump’s handpicked Commission of Fine Arts voted Thursday to approve a commemorative coin for the United States’ 250th birthday featuring the president’s likeness. And it’s going to ...
Abstract: Aerial image classification plays a vital role in applications such as building footprint extraction, water/soil analysis, 3D reconstruction. Accurate classification enables timely ...
After Russia invaded Ukraine in 2022, social media was littered with crude fakes that were presented as fresh images of the war but were either photoshopped phonies or mislabeled clips taken from ...
Abstract: In recent years, uncrewed aerial vehicle (UAV) technology has shown great potential for application in hyperspectral image (HSI) classification tasks due to its advantages of flexible ...
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