Attention Mechanisms and Transformers in Visual Arts
Attention mechanisms and transformers, initially popularized in natural language processing, have found significant applications in computer vision. The Vision Transformer (ViT) architecture applies the transformer model directly to image patches, allowing the network to focus on different parts of the image with varying degrees of attention. This enables better capture of global image dependencies compared to traditional CNNs. Attention mechanisms enhance image generation models by improving their ability to focus on critical image regions, leading to more coherent and detailed outputs in tasks such as image synthesis and segmentation.