This project showcases a sophisticated pipeline for object detection and segmentation using a Vision-Language Model (VLM) and the Segment Anything Model 2 (SAM2). The core idea is to leverage the ...

Spiking Neural Networks (SNNs), inspired by neuroscience principles, have gained attention for their energy efficiency. However, directly trained SNNs lag behind Artificial Neural Networks (ANNs) in ...

Abstract: Object detection in event streams has emerged as a cutting-edge research area, demonstrating superior performance in low-light conditions, scenarios with motion blur, and rapid movements.

Abstract: Object detection underwater is one of the most important tasks in various applications: marine biology, environ- mental monitoring, and underwater exploration. In this paper, we discuss a ...

Traffic monitoring plays a vital role in smart city infrastructure, road safety, and urban planning. Traditional detection systems, including earlier deep learning models, often struggle with ...

Introduction: Accurate vehicle analysis from aerial imagery has become increasingly vital for emerging technologies and public service applications such as intelligent traffic management, urban ...

The Patrick F. Taylor Foundation Object Project is a new interactive learning space opening on July 1. Here, we're highlighting five objects that will be on display—all of which have unique stories ...

Spending hours manually creating address objects on your Palo Alto Networks firewall? There’s a smarter, faster way! This guide will show you how to leverage the Pan-OS REST API and Python to automate ...

This project implements real-time object detection using OpenCV and a pre-trained SSD MobileNet V3 model. The application can identify and label various objects from a webcam feed or uploaded images ...