Visual big models, also known as deep learning models or visual transformers, are a highly anticipated technological breakthrough in artificial intelligence technology in recent years, particularly demonstrating enormous potential in the field of computer vision. Visual big models are a type of artificial intelligence model aimed at understanding and interpreting visual information. The big visual model operates based on deep learning principles and utilizes neural networks with a large number of parameters to analyze and understand visual content. Intended to automatically learn hierarchical structures from a wide range of datasets, enabling large visual models to detect complex patterns and connections in images. The visual big model is based on neural network architecture, especially the Transformer architecture, which consists of two parts: self attention mechanism and position encoding. The self attention mechanism enables the model to focus on important parts of the input data, while positional encoding helps the model understand the positional relationships of elements in the image. Through these two parts, the visual big model can capture.
