Self-Attention vs. Convolution

Explore the differences between self-attention and convolutional methods in computer vision. Learn how self-attention projects image data into key, query, and value vectors to capture global relationships across patches. Understand the computational costs of attention maps and the role of multihead attention in extracting diverse features. Gain insights into self-attention’s dynamic weights versus convolution’s fixed filters, enhancing your grasp of transformer applications in visual tasks.

We'll cover the following...

Comparing self-attention and convolution in computer vision
Understanding the cost of attention maps
- Implications of high-dimensional image embeddings
The role of multihead attention
A simple code implementation

Let's explore how we can employ self-attention in computer vision.

Comparing self-attention and convolution in computer vision

The process of generating self-attended feature maps involves a series of transformations applied to a 3D image representation, denoted as $X$ . Initially, $X$ is a 2D image transformed by weight matrices $W$ , with an added third dimension representing channel information, such as different colors.

The first step involves the extraction of three sets of weight matrices, namely $W_k$ , $W_q$ , and $W_v$ . Subsequently, these weight matrices are applied to the original image representation $X$ to form three distinct matrices: $K$ , $Q$ , and $V$ . The attention mechanism comes into play as it calculates an attention map based on the relationships between $K$ and $Q$ , highlighting the relevance of each element in the feature map. Following this, a dot product operation is applied between the original values $V$ and the attention map, focusing on important features determined by the attention mechanism. The final output is the self-attended feature map obtained through the weighted combination of $V$ using the attention map. This comprehensive process systematically captures significant patterns and relationships within the original image feature maps, providing a refined and context-aware representation.

1.Introduction

2.Overview of Transformer Networks

Mini Project

3.Transformers in Computer Vision

Project

4.Transformers in Image Classification

Mini Project

5.Transformers in Object Detection

6.Transformers in Semantic Segmentation

7.Spatio-Temporal Transformers

Mini Project

8.Wrap Up

Mock Interview

Self-Attention vs. Convolution

Comparing self-attention and convolution in computer vision