Window Based Transformer

Window-based transformers represent a computationally efficient approach to applying the power of transformer architectures to image and signal processing tasks. Current research focuses on optimizing these models for various applications, including image super-resolution, object detection, and medical image reconstruction, often through novel attention mechanisms and architectural modifications like dynamic window sizes or polar coordinate systems. This approach offers a compelling alternative to convolutional neural networks by balancing the ability to capture long-range dependencies with reduced computational cost, leading to improvements in accuracy and speed across diverse fields. The resulting advancements are impacting various applications, from autonomous driving to medical imaging.