Lectures related to Transformers in Vision

Transformers in Vision: Applications and Architectures

Covers the impact of transformers in computer vision, discussing their architecture, applications, and advancements in various tasks.

Computer Vision History Recap

Offers a historical overview of computer vision, exploring key developments and influential figures in the field.

Visual Intelligence: Machines and Minds

Explores visual intelligence, image formation, computer vision, and representation understanding in machines and minds.

Machine Learning for Feature Extraction

Explores machine learning for feature extraction, 3D vision, and neural networks in mobile robotics.

Computer Vision: Historical Insights and Project Inspirations

Explores the historical development of computer vision and inspires innovative project ideas.

Transformers in Vision

Explores the evolution of visual intelligence models, focusing on Transformers and their applications in computer vision and natural language processing.

Transformers in Vision

Explores Transformers in computer vision, focusing on 'Attention Is All You Need' architecture and its applications in visual tasks.

Visual Intelligence: Machines and Minds

Explores the history and techniques of computer vision, covering image formation, transformation, dynamic perspectives, and 3D estimation cues.

Edge Detection: Deep Learning Insights

Explores the evolution of edge detection techniques, from Canny to deep learning insights.

Feedback & Adaptation: Visual Intelligence

Covers feedback mechanisms in visual intelligence, human pose estimation, motor adaptation in legged robots, and PID controllers.

Deep Learning: Edge Detection and Neural Networks

Discusses edge detection techniques and the evolution of deep learning in neural networks.

Visual Prostheses: History and Intracortical Stimulation

Explores the history and challenges of visual prostheses, focusing on intracortical microstimulation for artificial vision.

Vision Research to Clinical Therapy

Explores vision research, optic nerve regeneration, and bio-realistic models in the mouse visual cortex, focusing on optic nerve stimulation and visual learning.

Perceptual Robotics: Integrating Vision and Action

Covers the integration of visual perception and robotic actions in embodied AI.

Neural Taskonomy and Historical Perspectives in Visual Intelligence

Covers Neural Taskonomy, the evolution of neural networks, and historical perspectives in visual intelligence.

PyTorch and Convolutional Networks

Covers PyTorch tensor data structure and training a CNN to classify images.

Deep Learning: Convolutional Neural Networks and Training Techniques

Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Computer Vision: Fundamentals and Applications

Covers the fundamentals of computer vision, illusions, challenges, applications, and history.

Improving Models of the Ventral Visual Pathway

Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.