Convolutional Transformer Block

Convolutional Transformer blocks aim to combine the strengths of convolutional neural networks (CNNs) and transformers for improved performance in various computer vision tasks. Current research focuses on developing efficient architectures that leverage the local feature extraction capabilities of CNNs with the long-range dependency modeling of transformers, often within single-branch networks to reduce computational cost. These hybrid blocks are proving valuable in applications ranging from image segmentation and anomaly detection to modulation recognition and weather image restoration, demonstrating a significant impact on the efficiency and accuracy of deep learning models for diverse visual data processing.