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: Hyperspectral image classification demands models capable of efficiently capturing complex spectral–spatial relationships and long-range dependencies. Despite significant advances in CNNs ...

Abstract: Various deep learning-based methods have greatly improved hyperspectral image (HSI) classification performance, but these models are sensitive to noisy training labels. Human annotation on ...

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.

A CNN insider reportedly issues a “wake-up call” to Kaitlan Collins as her presence at an exclusive no-journalists party sparks backlash and questions about her image. Samuel Alito raises question ...

Abstract: Feature representation is crucial for hyperspectral image (HSI) classification. However, existing convolutional neural network (CNN)-based methods are limited by the convolution kernel and ...

Abstract: Aerial image classification plays a vital role in applications such as building footprint extraction, water/soil analysis, 3D reconstruction. Accurate classification enables timely ...