Abstract: Deep learning models in computer vision face challenges such as high computational resource demands and limited generalization in practical scenarios. To address these issues, this study ...

Create a no-code AI researcher with two research modes and verifiable links, so you get quick answers and deeper findings ...

Brelyon Visual Engine uses NIMs capabilities along with real-time shader programming to extract data across different ...

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...

Every four years at the Cybathlon, teams of researchers and technology “pilots” compete to see whose brain-computer interface holds the most promise. Owen Collumb, a Cybathlon race pilot who has been ...

Dr. Xiaojun Qi is a Professor in the School of Computing and Director of the Computer Vision Laboratory at Utah State University (USU). She is an expert in artificial intelligence, specializing in ...

New research demonstartes the power of combining computer vision with generative models to address key inefficiencies in smart farming. Published in Applied Sciences, the study emphasizes how these AI ...

A deep learning system can accurately detect vision-threatening diabetic retinopathy. A dual-modality, deep learning system can accurately detect vision-threatening diabetic retinopathy (vtDR) using ...

This study introduces Popnet, a deep learning model for forecasting 1 km-gridded populations, integrating U-Net, ConvLSTM, a Spatial Autocorrelation module and deep ensemble methods. Using spatial ...