Hybrid CNN Transformer

Hybrid CNN-Transformer architectures combine the strengths of convolutional neural networks (CNNs) for local feature extraction and transformers for capturing global context, aiming to improve performance in various computer vision and signal processing tasks. Current research focuses on optimizing these hybrid models for efficiency, often employing techniques like lightweight attention mechanisms and efficient fusion strategies within U-Net or similar encoder-decoder structures. These advancements are significantly impacting fields like medical image analysis, remote sensing, and object detection by enabling more accurate and computationally efficient solutions for complex problems.