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Lecture
Transformers in Vision
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Related lectures (29)
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Computer Vision Basics: Image Processing and Feature Detection
Covers the basics of computer vision, focusing on image processing techniques and feature detection.
Language Models: From Theory to Computation
Explores the mathematics of language models, covering architecture design, pre-training, and fine-tuning, emphasizing the importance of pre-training and fine-tuning for various tasks.
Deep Learning: Edge Detection and Neural Networks
Discusses edge detection techniques and the evolution of deep learning in neural networks.
Generative Models: Self-Attention and Transformers
Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.
Transformers: Revolutionizing Attention Mechanisms in NLP
Covers the development of transformers and their impact on attention mechanisms in NLP.
Edge Detection: Deep Learning Insights
Explores the evolution of edge detection techniques, from Canny to deep learning insights.
Visual Intelligence: Machines and Minds
Explores the history and techniques of computer vision, covering image formation, transformation, dynamic perspectives, and 3D estimation cues.
NFNets: Removing BatchNorm for High-Performance Image Recognition
Explores NFNets as an alternative to BatchNorm in ResNets, achieving high performance on ImageNet.
Computer Vision History Recap
Offers a historical overview of computer vision, exploring key developments and influential figures in the field.
