Tag: Swin Transformer

A new deep learning architecture called TexTNet integrates local binary pattern texture features into a Swin Transformer's attention mechanism to ...

Researchers at the Vellore Institute of Technology developed three hybrid deep learning models combining convolutional networks, transformers, and clinical metadata ...

Deep learning models trained on 35,060 synthetic image pairs automatically remove salt-and-pepper noise, ring artifacts, and beam-hardening distortions from micro-CT ...

A dual-track deep learning network combining Swin Transformer global context with convolutional feature pyramids classifies pear leaf diseases with 92.69% ...

Researchers in Algeria have combined convolutional neural networks with a Swin Transformer to detect network intrusions with up to 99.69 ...

A new deep learning framework called MedFusionNet combines diffusion-based synthetic MRI generation with a fusion of three neural architectures to ...

Researchers have combined an Enhanced-Value attention scheme within a Swin Transformer with super-resolution-guided training to improve detection of tiny objects ...

A new multi-scale deep learning framework called Rain-PostEFNet substantially improves numerical weather prediction rainfall forecasts, with the largest gains in ...

Researchers have developed a Swin Transformer-based U-Net with a novel SECA cross-attention decoder that improves breast tumor segmentation accuracy across ...

Researchers in India have built a tri-backbone deep learning system with an intuitionistic fuzzy fusion module that classifies apple leaf ...