Example Concept: Vision Transformers (ViTs) use a self-attention mechanism to process images by dividing them into patches and treating each patch as a token, similar to words in NLP tasks. This allows ViTs to capture long-range dependencies across an image. In contrast, CNNs use convolutional layers to detect local patterns and features, building up to more complex representations through hierarchical layers. ViTs can outperform CNNs on large datasets due to their ability to model global context, but they may require more data and computational resources to train effectively.