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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DETR

Transformers

DETR2

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

What’s the primary focus of semantic segmentation?

What’s the primary focus of semantic segmentation?

Increasing spatial dimensions during decoding

Preserving fine-grained details during downsampling

Addressing vanishing gradients in shallow networks

What’s the role of skip connections in U-Net architecture for semantic segmentation?

What’s the role of skip connections in U-Net architecture for semantic segmentation?

Lack of inductive bias for local spatial features

What’s a key challenge associated with using transformer decoders in image segmentation?

What’s a key challenge associated with using transformer decoders in image segmentation?

Lack of inductive bias for capturing local spatial features

What’s a potential limitation of using transformer decoders in semantic segmentation?

What’s a potential limitation of using transformer decoders in semantic segmentation?

Test your understanding of transformer applications in image segmentation, addressing challenges, and understanding models like SETR and Segmenter.

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: Transformers in Semantic Segmentation