How transformer took over computer vision? CNN's struggle with long range dependency

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Why do we need transformers for vision?

To answer this, we first revisit Convolutional Neural Networks (CNNs) – the models that powered computer vision breakthroughs for almost a decade. CNNs have been the backbone of image classification, segmentation, and detection tasks, driving successes in models like AlexNet, VGG, ResNet, and beyond.

📌 In this lecture you will learn:

- How CNNs work using convolution operations, filters, and feature maps.
- Why convolutions are so powerful for extracting local patterns in images.
- The intuition behind kernels, stride, and receptive fields.
- The limitations of CNNs – difficulty in modeling global context, reliance on local patterns, and inefficiency when scaling to larger images.
- Why these shortcomings created the need for a new architecture.

We then discuss the motivation for transformers in vision. Unlike CNNs, transformers can capture long-range dependencies and global context more effectively, making them a natural fit for tasks where relationships across the entire image matter.

From there, we introduce the Vision Transformer (ViT) – the groundbreaking architecture from 2020 that reshaped how we think about computer vision. You will see how images can be split into patches, treated like tokens, and processed with self-attention, just like text sequences in large language models.

📖 We also look at the original ViT paper, its reception in the research community, and the massive impact it had with 65k+ citations.

By the end of this lecture, you will have:

- A clear understanding of how CNNs operate.
- A structured view of their strengths and weaknesses.
- A solid motivation for why transformers are now essential for vision.
- A first look at the Vision Transformer architecture that will be explored in detail in upcoming lectures.

This lecture sets the stage for the entire bootcamp – where we will move step by step from CNNs, to transformers, to advanced architectures for detection, segmentation, video understanding, multimodal learning, and generative vision models.

🔥 Two Versions of the Bootcamp

Free Version (YouTube Playlist) – You can follow along with every lecture, uploaded sequentially, in a dedicated playlist.

Pro Version (https://vision-transformer.vizuara.ai) – Includes everything from the free version plus:

- Detailed handwritten notes (Miro)
- Private GitHub repository with all code
- Private Discord community for collaboration and doubt clearance
- PDF e-book on Transformers for Vision & Multimodal LLMs
- Hands-on assignments with grading
- Official course certificate
- Email support from Team Vizuara

👉 Join the Pro Bootcamp here:
http://vision-transformer.vizuara.ai/

YouTube url:	https://youtu.be/P1pqJ3NlTdU?si=kI_wpXbvVKCsqHLI
Created:	2. 10. 2025 05:31:56

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How transformer took over computer vision? CNN's struggle with long range dependency