Researchers propose a Vision Transformer approach that detects FFF surface defects in real time with on-demand explainability ...

Vision Transformers, or ViTs, are a groundbreaking learning model designed for tasks in computer vision, particularly image recognition. Unlike CNNs, which use convolutions for image processing, ViTs ...

Computer vision continues to be one of the most dynamic and impactful fields in artificial intelligence. Thanks to breakthroughs in deep learning, architecture design and data efficiency, machines are ...

The object detection required for machine vision applications such as autonomous driving, smart manufacturing, and surveillance applications depends on AI modeling. The goal now is to improve the ...

In the last decade, convolutional neural networks (CNNs) have been the go-to architecture in computer vision, owing to their powerful capability in learning representations from images/videos.

Transformers, first proposed in a Google research paper in 2017, were initially designed for natural language processing (NLP) tasks. Recently, researchers applied transformers to vision applications ...

Vision transformers (ViTs) are powerful artificial intelligence (AI) technologies that can identify or categorize objects in images -- however, there are significant challenges related to both ...

A new study reports a ViT-YOLOv8 framework for smoke and fire detection, achieving 98.5% precision and improving early ...