Transformers in Vision

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Related lectures (29)

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Deep Visual Recognition: Interpretability

Explores deep visual recognition, interpretability, CNN architectures, visual dictionaries, and attention mechanisms.

Neural Networks: Two Layers Neural Network

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Visual Intelligence and Learning: Insights and Applications

Explores visual intelligence, robotics perception, and multi-task learning techniques in computer vision.

Classical Vision Theories: Understanding Perception and Cognition

Provides an overview of classical vision theories, exploring how perception is shaped by psychological and environmental factors.

Deep Learning: Exploring Vision and Language Transformers

Covers advanced transformer architectures in deep learning, focusing on Swin, HUBERT, and Flamingo models for multimodal applications.

Human Vision: From World to Images

Explores human vision from the retina to the visual cortex, covering topics like color vision, sensitivity to different wavelengths, and comparing human and computer vision processes.

Geometric Stereo: Depth Recovery Techniques

Discusses geometric stereo principles and depth recovery techniques using stereo vision methods.

Building Technology III: Visual Comfort

Explores the physics of building technology and the physiology of vision, emphasizing visual comfort and lighting effects.

Transformers: Unifying Machine Learning Communities

Covers the role of Transformers in unifying various machine learning fields.