The LLaMA family, a collection of foundation language models ranging from 7B to 65B
parameters, has become one of the most powerful open-source large language models …

Accurate brain tumor segmentation in MRI images is crucial for effective treatment planning
and monitoring. Traditional methods often encounter challenges due to the complexity and …

Although image super-resolution (SR) problem has experienced unprecedented restoration
accuracy with deep neural networks it has yet limited versatile applications due to the …

Aerial photography is a remote sensing technique used for target detection, enabling both
qualitative and quantitative analysis. The segmentation process is considered one of the …

Malware attacks have escalated significantly with an increase in the number of internet
users and connected devices. With the increasingly different types of malware released by …

The integration of deep learning (DL) into image processing has driven transformative
advancements, enabling capabilities far beyond the reach of traditional methodologies. This …

Abstract Video Snapshot Compressive Imaging (SCI) aims to use a low-speed 2D camera to
capture high-speed scene as snapshot compressed measurements, followed by a …

This review explores the integration of human posture recognition and classification
technologies in performing arts education, focusing on the advancements in deep learning …

To tackle the complexity and limited applicability of high-precision segmentation models for
damaged road markings, this study proposes a Multi-level Adaptive Lightweight Network …

Existing super-resolution (SR) methods optimize all model weights equally using $\mathcal
{L} _1 $ or $\mathcal {L} _2 $ losses by uniformly sampling image patches without …