Vision Transformer Part 2
— let’s understand the attention mechanism
Role of Attention Mechanism in ViTs
It started with the paper named Attention is all you need. It refers to a key component that allows the model to focus on different parts of an image when making decisions. This mechanism is inspired by the human brain’s ability to focus on certain regions of an image while processing visual information. It works by calculating the importance of each part of the input image relative to the others. This is done by computing attention scores between different tokens (representations of image patches) in the model. The attention scores are then used to weight the input features and decide how much focus to give to each token when processing the image.
By using the attention mechanism, ViTs can capture long-range dependencies in images and better understand the spatial relationships between different parts of the input. This allows the model to effectively recognize objects, patterns, and structures in images and make accurate predictions based on this information. It plays a crucial role in enabling the model to process and analyze visual data efficiently, leading to improved performance in various computer vision tasks.
Attention Module
It consists of three main components: key, value, and query. These components are used to compute the attention scores between different tokens in the model and determine how much focus to give to each token when processing the input image.
1. Query: The query component is responsible for generating a representation of a token that will be compared with other tokens in the model. The query is typically derived from the input features and serves as a guide for determining which parts of the image to pay attention to.
2. Key: The key component represents another set of token representations that are used to compute the similarity between the query and other tokens. The key helps to identify which parts of the image are most relevant to the query token and influences the attention scores assigned to each token.
3. Value: The value component contains information about the token that will be used in the final output of the attention mechanism. The value is weighted according to the attention scores determined by the comparison of the query and key, and it helps to aggregate information from different tokens in the model.
During the attention computation process, the query, key, and value components interact with each other to calculate attention scores for each token in the model. The attention scores reflect the importance of each token relative to the query token and influence how much attention should be given to that particular token during processing.
Conclusions:
By using the key, value, and query components in the attention module, ViTs can effectively capture long-range dependencies in images, understand spatial relationships, and make accurate predictions based on the input data. The attention mechanism plays a crucial role in enabling the model to focus on relevant parts of the image and extract meaningful information for various computer vision tasks. Their ability to model global dependencies and capture fine-grained details in images makes them a powerful tool for a wide range of vision tasks.
We will discuss about the multi-head attention, encoder block in part 3 of the Vision Transformer series.
We will compare different ViT architectures in part 4 of the Vision Transformer series.