Transformer Architectures in Vision (ViT)
Definition: Transformer Architectures in Vision, often abbreviated as ViT, are a class of deep learning models that apply transformer architectures, originally designed for natural language processing tasks, to computer vision tasks. ViT models have shown remarkable performance in image classification, object detection, and other vision tasks, often surpassing traditional convolutional neural networks (CNNs).
Explanation: The key innovation of ViT is the application of the transformer’s self-attention mechanism to vision tasks. Unlike CNNs, which process images in a local and hierarchical manner, transformers treat input data as a sequence of tokens, allowing them to capture long-range dependencies between pixels in an image.
In a ViT model, an image is divided into a fixed number of patches, each of which is treated as a token. These tokens are linearly embedded and processed through a series of transformer layers. The output of the final layer is used for classification or other tasks.
Benefits: ViT models offer several advantages over traditional CNNs:
Long-range dependencies: ViT models can capture relationships between pixels that are far apart, which can be crucial for understanding complex scenes.
End-to-end training: ViT models can be trained end-to-end with gradient descent, without the need for handcrafted features or region proposals.
Transfer learning: ViT models pre-trained on large image datasets can be fine-tuned on specific tasks, often achieving state-of-the-art performance.
Challenges: Despite their advantages, ViT models also have some challenges:
Computational cost: ViT models can be computationally expensive, especially for large images, due to the quadratic complexity of self-attention.
Data requirements: ViT models often require large amounts of training data to achieve good performance.
Examples: The original ViT model, introduced by Google Research, is a popular example of this architecture. Other variants include DeiT (Data-efficient Image Transformers), which is designed to work well with less data, and Swin Transformers, which introduce a hierarchical structure to reduce computational cost.
Related Terms: Transformer, Self-Attention, Deep Learning, Computer Vision, Image Classification, Object Detection
Further Reading:
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- Data-efficient Image Transformers: A Promising Start
- Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
References:
- Dosovitskiy, A., et al. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv preprint arXiv:2010.11929.
- Touvron, H., et al. (2020). Data-efficient Image Transformers: A Promising Start. arXiv preprint arXiv:2012.12877.
- Liu, Z., et al. (2021). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. arXiv preprint arXiv:2103.14030.
