Convolutional Transformer

Convolutional Transformers (ConvTransformers) are hybrid neural network architectures combining the strengths of convolutional neural networks (CNNs) and transformers to improve performance in various tasks, primarily by leveraging CNNs' efficiency for local feature extraction and transformers' ability to model long-range dependencies. Current research focuses on developing efficient ConvTransformer models, often employing techniques like linear attention mechanisms and novel attention modules to reduce computational costs while maintaining accuracy. These advancements are significantly impacting diverse fields, including image processing (super-resolution, segmentation), time-series analysis (EEG signal processing, action recognition), and other applications where both local and global context are crucial for accurate predictions.