Vision Transformers (ViT) is a transformer-based architecture that applies the transformer model to computer vision tasks, such as image classification. It was introduced in the research paper titled "An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale" by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby, published in 2020.
Overview:
ViT represents images as sequences of fixed-size non-overlapping patches and feeds them into the transformer model, which is originally designed for sequential data. The transformer processes these patches to perform image recognition tasks, such as image classification. By leveraging the transformer's attention mechanism, ViT can capture global context and long-range dependencies, making it competitive with traditional convolutional neural networks (CNNs) on various vision tasks.
Technical Details:
1. Patch Embeddings: ViT breaks down the input image into smaller, fixed-size patches. Each patch is then linearly embedded into a lower-dimensional space. This embedding converts the image patches into a sequence of tokens, which are the input tokens for the transformer.
2. Positional Embeddings: Similar to the original transformer, ViT introduces positional embeddings to inform the model about the spatial arrangement of the patches. Since transformers don't inherently have any information about the sequence order, positional embeddings provide this information so that the model can understand the spatial relationships between different patches.
3. Pre-training and Fine-tuning: ViT is usually pre-trained on a large-scale dataset using a variant of the self-supervised learning approach called "Jigsaw pretext task." This pre-training step helps the model learn meaningful representations from the image data. After pre-training, the ViT can be fine-tuned on downstream tasks such as image classification with a smaller labeled dataset.
4. Transformer Architecture: The core of ViT is the transformer encoder, which consists of multiple layers of self-attention and feed-forward neural networks. The self-attention mechanism allows the model to capture dependencies between different patches and focus on relevant parts of the image. The feed-forward neural networks introduce non-linearities and increase the model's expressiveness.
5. Training Procedure: During pre-training, ViT is trained to predict the correct spatial arrangement of shuffled patches (the Jigsaw pretext task). This task encourages the model to learn visual relationships and helps it to generalize better to unseen tasks. After pre-training, the model's weights can be fine-tuned using labeled data for specific tasks, such as image classification.
Example:
Let's say we have a 224x224 RGB image. We divide the image into non-overlapping patches, say 16x16 each, resulting in 14x14 patches for this example. Each of these patches is then linearly embedded into a lower-dimensional space (e.g., 768 dimensions) to create a sequence of tokens. The positional embeddings are added to these token embeddings to represent their spatial locations.
These token embeddings, along with the positional embeddings, are fed into the transformer encoder, which processes the sequence through multiple layers of self-attention and feed-forward neural networks. The transformer learns to attend to important patches and capture long-range dependencies to recognize patterns and features in the image.
Finally, after pre-training and fine-tuning, the ViT model can be used for image classification or other computer vision tasks, achieving state-of-the-art performance on various benchmarks.
Overall, Vision Transformers have shown promising results and opened up new possibilities for applying transformer-based models to computer vision tasks, providing an alternative to traditional CNN-based approaches.
