IEEE

HC-Mamba: Remote Sensing Image Classification via Hybrid Cross-Activation State–Space Model

Abstract: Remote sensing image classification plays a crucial role in scene understanding task interpretation. Currently, remote sensing image classification is constrained by the complexity, ...
Frontiers

Enhanced hyperspectral image classification for coastal wetlands using a hybrid CNN-transformer approach with cross-attention mechanism

1 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences (CAS), Beijing, China 2 ...
Frontiers

Comparative analysis of convolutional neural networks and transformer architectures for breast cancer histopathological image classification

The First Hospital of Hunan University of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, China Background: Breast cancer remains the most prevalent malignancy in women globally, ...
IEEE

Image Classification Model Based on Contrastive Learning With Dynamic Adaptive Loss

Abstract: As one of the core tasks in vision recognition, image classification is widely used in various scenarios. Most existing mainstream image classification models use the Convolutional Neural ...
Scientific Research Publishing

A Wavelet-Based Two-Stage Vision Transformer Model for Histological Subtypes Classification of Lung Cancers on CT Images ()

1 Faculty of Informatics, The University of Fukuchiyama, Kyoto, Japan. 2 School of Radiological Technology, Gunma Prefectural College of Health Sciences, Gunma, Japan. 3 School of Health Sciences, ...
GitHub

Efficient Binary Video Classification on a Local GPU-Enabled PC Using PyTorch

This repository provides an efficient binary video classification pipeline using PyTorch, optimized for local GPU-enabled PCs. It includes preprocessing and model inference tools for classifying ...
GitHub

PyTorch LatinLetters Dimensionality Reduction

Example of classification with ~91% Acuracy: Data separation between training and validation datatasets are .85 to .15 with 15k of sample images stored as numpy arrays in csv file Made by using ...