Microsoft

Securing Public Cloud Networks with Efficient Role-based Micro-Segmentation

Securing network traffic within data centers is a critical and daunting challenge due to the increasing complexity and scale of modern public clouds. Micro-segmentation offers a promising solution by ...
IEEE

Asymmetric Dual-Encoder Network with Clustering and Mutual Contrast Loss for the Semantic Segmentation of Remote-Sensing Images

Abstract: In recent years, the semantic segmentation of multimodal remote-sensing images using convolutional methods has received significant attention. Owing to the localized nature of convolutional ...
Hacker

How to Implement AI-Powered Image Generation in the Browser Using React, Vite, and DALL·E

Full-stack developer, passionate about AI and learning new things. Powered by coffee and curiosity.
GitHub

SAMA-UNet: Self-Adaptive Mamba-like Attention UNet

Official PyTorch implementation of SAMA-UNet: A novel U-shaped architecture for medical image segmentation that integrates Self-Adaptive Mamba-like Attention and Causal-Resonance Learning.
Frontiers

Advanced hyperspectral image processing and machine learning approaches for early detection of wheat stem rust

1 Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia 2 All-Russian Institute of Plant Protection, Saint Petersburg, Russia However, despite rapid methodological advances, ...
GitHub

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation

Few-shot Adaptation of Training-frEe SAM (FATE-SAM) is a versatile framework for 3D medical image segmentation that adapts the pretrained SAM2 model without fine-tuning. By leveraging a support ...
IEEE

Clustering-Based Adaptive Query Generation for Semantic Segmentation

Abstract: Semantic segmentation is one of the crucial tasks in the field of computer vision, aiming to label each pixel according to its class. Most recently, several semantic segmentation methods, ...