Lightweight CNN Transformer

Lightweight CNN-Transformer models aim to combine the strengths of Convolutional Neural Networks (CNNs) for local feature extraction and Transformers for global context understanding, while minimizing computational cost and model size. Current research focuses on developing efficient hybrid architectures, often employing streamlined CNN backbones and incorporating techniques like partial self-attention or adaptive local-global information flow to reduce complexity. These advancements are significant for deploying deep learning models on resource-constrained devices, enabling applications in diverse fields such as medical image segmentation, remote sensing, and federated learning.