This course covers transformer networks in computer vision, from basics to advanced applications, focusing on attention mechanisms and Python libraries.

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DETR

Transformers

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This is a comprehensive course on transformer networks 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, multihead 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 spatio-temporal transformers, bridging the gap between static images and dynamic data.

By the end of the course, you’ll be equipped with the knowledge and skills to leverage transformer networks across diverse applications in deep learning and artificial intelligence.

Transformers for Computer Vision Applications

To represent the time frames of each video frame

To provide spatial information to the transformer encoder

To facilitate attention across spatial dimensions

To introduce a time dimension and enable the modeling of temporal relations

What’s the purpose of positional embeddings in the Video Transformer Network (VTN) architecture?

What’s the purpose of positional embeddings in the Video Transformer Network (VTN) architecture?

By processing each frame with a recurrent neural network

By combining 2D embeddings with positional embeddings

By performing direct classification without temporal consideration

How does the VTN architecture incorporate temporal information for video classification?

How does the VTN architecture incorporate temporal information for video classification?

It’s a token reserved for natural language processing.

It’s a token added to represent the global context of the video.

It’s a token used for convolutional neural network operations.

It’s a token indicating the start of a video sequence.

What does the **CLS** token represent in the context of the Video Transformer Network (VTN) architecture?

What does the CLS token represent in the context of the Video Transformer Network (VTN) architecture?

Output of the spatio-temporal transformer

In the simulated code example, what does the "temporal representation" represent?

In the simulated code example, what does the “temporal representation” represent?

Test your understanding of transformer applications in video analysis.

Introduction

Overview of Transformer Networks

Neural Machine Translation with a Transformer and Keras

Transformers in Computer Vision

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: Spatio-Temporal Transformers