Offical implementation of "Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection" we also add our config in https://github.com/zcablii/LSKNet and ...

Visual Attention Networks (VANs) leveraging Large Kernel Attention (LKA) have demonstrated remarkable performance in diverse computer vision tasks, often outperforming Vision Transformers (ViTs) in ...

Abstract: While transformers demonstrate outstanding performance across various audio tasks, their application to neural vocoders remains challenging. Neural vocoders require the generation of long ...

This library brings Spatially-sparse convolutional networks to PyTorch. Moreover, it introduces Submanifold Sparse Convolutions, that can be used to build computationally efficient sparse ...

Abstract: Although hyperspectral remote sensing images have rich spectral features, for small samples of remote sensing images, feature selection, feature mining, and feature integration are very ...

Data assimilation (DA) is used to obtain the best states and their uncertainty of the Earth system by incorporating possible states measured through numerical-model-based forecasting and observations ...

The weight-sharing property of convolutional neural networks (CNNs) has been a revolutionary concept in the field of deep learning and computer vision. This article explores the genesis, advantages, ...

You’ll need Node/NPM installed to start. Then you can create a new directory and run npm init, and accept the defaults. Next, install TensorFlow by entering npm i ...

In the previous column, we introduced the basic building blocks of an artificial neural network (ANN): neurons computing linear combinations of inputs, followed by a nonlinear activation function 1.

Steady state visually evoked potentials (SSVEPs) based early glaucoma diagnosis requires effective data processing (e.g., deep learning) to provide accurate stimulation frequency recognition. Thus, we ...