Learn about transformer networks, self-attention, multi-head attention, and spatiotemporal transformers in this course, focusing on their applications in computer vision and deep learning.

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Transformers

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This is a comprehensive course on vision transformers and their use cases in computer vision. You’ll begin by exploring the rise of transformers and attention mechanisms and their role in deep neural networks. 
You’ll gain insights into self-attention mechanisms, multi-head attention, and the pros and cons of transformers building a strong foundation. Next, you’ll discover how transformers reshape image analysis. Comparing self-attention with convolutional encoders and understanding spatial vs. channel vs. temporal attention, you’ll grasp nuances in applying transformer architectures to visual data. 

The course also explores spatiotemporal transformers, bridging the gap between static images and dynamic data. After completing this course, you’ll have the knowledge and skills to leverage transformer networks across diverse applications in deep learning and artificial intelligence.

Transformers for Computer Vision Applications

In the universal encoder-decoder architecture, what does the encoder do?

In the universal encoder-decoder architecture, what does the encoder do?

By generating new images from text descriptions

By performing tasks such as object detection, segmentation, and image generation

How does the universal encoder-decoder architecture extend its applications beyond classification tasks in computer vision?

How does the universal encoder-decoder architecture extend its applications beyond classification tasks in computer vision?

They establish global relationships between different areas of the image.

They generate embedded self-attention feature maps.

They function as feature detectors seeking specific features.

What’s the primary purpose of projections $(Q, K, V)$ in the context of self-attention in computer vision?

What’s the primary purpose of projections <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>Q</mi><mo separator="true">,</mo><mi>K</mi><mo separator="true">,</mo><mi>V</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(Q, K, V)</annotation></semantics></math>(Q,K,V) in the context of self-attention in computer vision?

The computational intensity of generating large attention maps

What’s a key challenge in generating attention maps for high-dimensional image embeddings in computer vision?

What’s a key challenge in generating attention maps for high-dimensional image embeddings in computer vision?

Both involve dynamic connectivity and attention.

Both use global connectivity and dynamic weights.

How does multihead attention in computer vision resemble convolution channels in natural language processing (NLP)?

How does multihead attention in computer vision resemble convolution channels in natural language processing (NLP)?

Test your knowledge of universal encoder-decoder architecture, self-attention, and computational challenges in computer vision.

Introduction

Overview of Transformer Networks

Neural Machine Translation with a Transformer and Keras

Transformers in Computer Vision

Vision Transformer for Image Classification

Transformers in Image Classification

Fine-Tuning Vision Transformers for Image Classification

Transformers in Object Detection

Transformers in Semantic Segmentation

Spatio-Temporal Transformers

Object Detection with Vision Transformers

Wrap Up

Quiz: Encoder-Decoder Architecture and Attention Mechanism in Computer Vision