Introduction to the Convolutional Vision Transformer (CvT)
The Convolutional Vision Transformer, or CvT for short, is a new type of architecture that combines the best of both convolutional neural networks (CNNs) and Transformers. The CvT design introduces convolutions into two core sections of the ViT (Vision Transformer) architecture to achieve spatial downsampling and reduce semantic ambiguity in the attention mechanism. This allows the model to effectively capture local spatial contexts while processing large-scale image data.
How CvT Works
First, the Transformers are divided into multiple stages forming a hierarchical structure of Transformers. Each stage starts with a convolutional token embedding that performs an overlapping convolution operation with stride on a 2D-reshaped token map, followed by layer normalization. This helps the model capture local information while progressively reducing the sequence length and increasing the dimension of token features across stages, which is similar to how CNNs work.
Second, the linear projection prior to every self-attention block in the Transformer module is replaced with a proposed convolutional projection, which employs a s × s depth-wise separable convolution operation on a 2D-reshaped token map. This allows the model to further capture local spatial context and reduce semantic ambiguity in the attention mechanism, and it also improves computational efficiency.
The proposed convolutional projection can be used to subsample the key and value matrices, reducing the computational complexity by 4× or more, with minimal degradation of performance.
The Advantages of CvT
The main advantage of CvT is its ability to achieve state-of-the-art performance with fewer parameters and computations than the previous generation of CNNs and Transformers-based networks. Thanks to its hierarchical structure and convolutional token embedding, CvT can capture both local and global features in images while eliminating the need for designing a large number of specialized feature extraction networks.
Moreover, CvT is more interpretable than previous architectures, meaning that it is easier to understand how the model makes its predictions. This is because the attention mechanism of the Transformer in CvT allows us to visualize which parts of the image are important for the model to focus on.
Applications of CvT
CvT has been applied to various image recognition problems, showing excellent performance in contrast to other state-of-the-art methods. Some examples of its potential applications include:
- Image classification
- Object detection and segmentation
- Semantic segmentation
- Pose estimation
- Medical image analysis
The promising results of CvT show that it has the potential to become a widely adopted method for large-scale image recognition tasks.
The Convolutional Vision Transformer (CvT) is a new architecture that combines the strengths of both convolutional neural networks and Transformers. Its hierarchical structure and convolutional token embedding allow it to capture local and global features in images while reducing computational complexity. The promising results of CvT show that it has the potential to become a widely adopted method for large-scale image recognition tasks.
