Calendar

Lecture 1Advanced Computer Vision: Introduction 1

Saining Xie

Lecture 1Advanced Computer Vision: Introduction 2

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The Overview: Past, Present and Paths Forward for Computer Vision

Reading materials

• ImageNet Classification with Deep Convolutional Neural Networks
• Deep Residual Learning for Image Recognition
• Attention is All You Need
• The Illustrated Transformer
• The Philosophy of Deep Learning

Lecture 2Computer Vision Fundamentals

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Filtering, Detectors, Descriptors

Why Representation Learning Matters

Lecture 3Introduction to Deep Learning

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Neural Networks

Backpropagation

Auto-Differentiation

• Autodiff
• Backpropagation by Andrej Karpathy
• Deep Learning Reference Book 1
• Deep Learning Reference Book 2

Lecture 4Training Deep Neural Networks

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Optimization, Initialization, Regularization & Normalization

• Deep Learning Reference Book 1
• Deep Learning Reference Book 2
• Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
• Group Normalization
• Adam: A Method for Stochastic Optimization
• A ConvNet for the 2020s

Lecture 5Remote LectureDetection and Segmentation

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ECCV Conference field report

Lecture 6Attention and Transformer Deep Dive

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Lecture 7Self-supervised Learning and Multi-modal Learning

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Lecture 8Generative Models 1

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Generative Adversarial Networks (GANs)

Lecture 9Generative Models 2

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Variational Autoencoders (VAEs)

Diffusion Models

Flow-based Models

Lecture 10Visualizing and Understanding Neural Networks

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Lecture 11Motion & Deep Learning on Spatiotemporal Data

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Lecture 123D Vision

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Cameras and Meshes

Point Cloud

NeRF

Gaussian Splatting

Lecture 13Guest Lecture: Topic TBD

TBD

Lecture 14Final Project Presntation

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