IEEE

Learning a 3-D-CNN and Convolution Transformers for Hyperspectral Image Classification

Abstract: Hyperspectral image (HSI) classification is an important but challenging task. Conventional convolutional neural networks (CNNs) are able to extract local spectral spatial features but ...
Yakima Herald

It Happened Here: 'Convolutions' sculpture added to Yakima's historic district

If it’s ‘Anywhere, America,’ if it’s Jack in the Box and homogenized, why in the hell would anybody come back?” sculptor Will ...
IEEE

A Hybrid Discrete Singular Convolution-FDFD Method for Large-Scale Electromagnetic Problems

Abstract: In this article, a hybrid discrete singular convolution-finite-difference frequency domain (DSCFD) method with high-order accuracy is proposed. The proposed method discretizes the curl ...
GitHub

Virtual Sparse Convolution for Multimodal 3D Object Detection

VirConv-L: A light-weight multimodal 3D detector based on Virtual Sparse Convolution. VirConv-T: A improved multimodal 3D detector based on Virtual Sparse Convolution and transformed refinement scheme ...
GitHub

ESC: Emulating Self-attention with Convolution for Efficient Image Super-Resolution

In this paper, we tackle the high computational overhead of transformers for lightweight image super-resolution. (SR). Motivated by the observations of self-attention's inter-layer repetition, we ...