Cross-Modal Data Understanding Advances Through Bukun Ren’s Review of Visual Language Models

A study on visual language models explores how shared semantic frameworks improve image–text understanding across ...
AZoSensors

Sensor-Based Nonlinear Metrics Improve Fall Risk Assessment

This study highlights non-linear center-of-pressure features that enhance clinical assessment of fall risk in older adults, ...
ahajournals.org

Synthetic Contrast-Free LGE via Diffusion-Based Framework in Acute MI for Image Quality and Quantitative Scar Analysis

This study aims to develop a diffusion model-based framework for generating late gadolinium enhancement (LGE)-like images without contrast. The resulting synthetic images are then comprehensively ...
VentureBeat

NYU’s new AI architecture makes high-quality image generation faster and cheaper

Researchers at New York University have developed a new architecture for diffusion models that improves the semantic representation of the images they generate. “Diffusion Transformer with ...
Digital Trends

Microsoft AI debuts its Nano Banana rival, and it’s already a top text-to-image model

What’s happened? Microsoft AI has unveiled the slightly clunkily named MAI-Image-1, its in-house text-to-image system. The pitch is straightforward, generate useful pictures quickly, not flashy demos ...
C&EN

Deep Learning-Based Shape Classification for Hyperspectral-Imaged Microplastics

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. Figure 1 illustrates the overall workflow of the hyperspectral ...
IEEE

Scattering Mechanism Inspired Non-Gaussian Diffusion Model for Polarimetric SAR Image Classification

Abstract: The diffusion model has achieved excellent performance in natural image processing, which can learn the noise distribution through the degradation and restoration processes. However, the ...
IEEE

Self-Supervised Learning of Contrast-Diffusion Models for Land Cover Classification in SAR Images

Abstract: Deep learning (DL) methods have been widely applied to synthetic aperture radar (SAR) land cover classification. The complexity of SAR data and the limited availability of labeled samples ...