First, similar to how the Transformer works, the Vision Transformer is supervised, meaning the model is trained on a dataset of images and their corresponding labels. Convert the patch into a vector ...

In the last decade, convolutional neural networks (CNNs) have been the go-to architecture in computer vision, owing to their powerful capability in learning representations from images/videos.

The increase in skin conditions, especially skin cancers, shows the need for accurate diagnostics. Traditional imaging methods struggle to capture complex skin lesion patterns, leading to potential ...

Transformers, first proposed in a Google research paper in 2017, were initially designed for natural language processing (NLP) tasks. Recently, researchers applied transformers to vision applications ...