Shui Xiuying

You are already at the latest version Vision Transformers (ViTs) have emerged as a powerful alternative to convolutional neural networks (CNNs) for visual recognition tasks due to their ability to model long-range dependencies in images through self-attention. However, the computational complexity and memory consumption of ViTs scale quadratically with the number of input patches, making them inefficient, especially for high-resolution images.