Self-Attention in Vision Models
Summary
Self-attention has emerged as a powerful alternative to traditional convolutional approaches in vision models. Recent research has demonstrated that self-attention can be used as a standalone primitive in vision tasks, replacing spatial convolutions entirely. Pure self-attention models have shown promising results, outperforming convolutional baselines on tasks such as image classification and object detection while using fewer parameters and computational resources. The Vision Transformer (ViT) architecture, which applies transformers directly to sequences of image patches, has achieved excellent performance on various image recognition benchmarks when pre-trained on large datasets. These findings suggest that self-attention is a valuable tool for computer vision practitioners, offering competitive performance and efficiency compared to conventional convolutional neural networks.