Frontiers

Advanced AI for missing modality learning and uncertainty‑aware medical image analysis

Modern medical imaging increasingly relies on artificial intelligence to support detection, diagnosis, and prognostic ...
GitHub

Cut and Learn for Unsupervised Image & Video Object Detection and Instance Segmentation

We propose MaskCut approach to generate pseudo-masks for multiple objects in an image. CutLER can learn unsupervised object detectors and instance segmentors solely on ImageNet-1K. CutLER exhibits ...
IEEE

CGNet: A Correlation-Guided Registration Network for Unsupervised Deformable Image Registration

Abstract: Deformable medical image registration plays a significant role in medical image analysis. With the advancement of deep neural networks, learning-based deformable registration methods have ...
IEEE

A Superpixel-Guided Unsupervised Fast Semantic Segmentation Method of Remote Sensing Images

Abstract: Semantic segmentation is one of the fundamental tasks of pixel-level remote sensing image analysis. Currently, most high-performance semantic segmentation methods are trained in a supervised ...
The Next Web

Unconventional AI releases its first model, built on an oscillator architecture its founder says could cut power use by a factor of a thousand

Naveen Rao's Unconventional AI has released Un-0, an image generation model running on a simulated oscillator chip architecture it claims could slash power.