An Ensemble Learning Tool for Land Use Land Cover Classification Using Google Alpha Earth Foundations Satellite Embeddings ...

Labeling images is a costly and slow process in many computer vision projects. It often introduces bias and reduces the ability to scale large datasets. Therefore, researchers have been looking for ...

In this tutorial, we explore the power of self-supervised learning using the Lightly AI framework. We begin by building a SimCLR model to learn meaningful image representations without labels, then ...

Objective: Deep learning (DL) has introduced new possibilities for estimating human joint moments - a surrogate measure of joint loads. However, traditional methods typically require extensive ...

Presentation of the best paper award at the RoboCup 2025 symposium. An important aspect of autonomous soccer-playing robots concerns accurate detection of the ball. This is the focus of work by Can ...

Abstract: In out-of-distribution detection, methods based on the Mahalanobis distance have excellent performance for detecting out-of-distribution data. The Mahalanobis distance measures the distance ...

Introduction: Accurate mapping of the spatial distribution of diverse cell types is essential for understanding the cellular organization of brain. However, the cellular heterogeneity and the ...

Leibniz-Centre for Agricultural Landscape Research (ZALF) e.V. Email: stefan [dot] stiller [at] zalf [dot] de, stillsen [at] gmail [dot] com This repository contains the code for the study ...

Blended learning allows teachers to combine the best of face-to-face and online instruction, but when it’s also self-paced, it opens up new possibilities for differentiation, mastery-based progression ...

Falls pose a significant public health concern, leading to numerous injuries and fatalities annually. Most existing fall detection methods often rely on supervised learning, necessitating extensive ...