A visual transformer is a deep learning neural network architecture that adapts the transformer model and its self-attention mechanisms to process visual data for computer vision tasks. Rather than relying primarily on standard convolutional operations, a visual transformer divides an input image into a sequence of smaller patches, converts these patches into vector embeddings analogous to word tokens in natural language processing, and incorporates positional information. These token representations are then processed through transformer layers, allowing the network to model relationships across all image regions simultaneously and capture long-range contextual dependencies. This approach enables effective feature extraction and representation learning across various visual applications, including image classification, object detection, and segmentation.
