Convolution Free

Convolution-free approaches in image processing and computer vision aim to replace traditional convolutional neural networks (CNNs) with alternative architectures, primarily focusing on transformer-based models that leverage attention mechanisms. Current research emphasizes the development and application of these transformer models for various tasks, including image compression, denoising, segmentation, and object recognition, often demonstrating competitive or superior performance compared to CNN-based methods. This shift is significant because it offers potential advantages such as improved handling of long-range dependencies and reduced computational complexity in certain applications, leading to more efficient and effective image analysis techniques.