Visual Intelligence and Learning: Insights and Applications

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Related lectures (30)

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Transformers: Unifying Machine Learning Communities

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

Neuroscience and AI: Bridging the Gap

Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.

Brain Intelligence: Continual Learning of Representational Models

Delves into the continual learning of representational models after deployment, highlighting the limitations of current artificial neural networks.

Deep Visual Recognition: Interpretability

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

Shape from Shading: Recovering 3D Information from 2D Images

Covers the techniques for recovering 3D shape information from 2D images using shading models and modern deep learning approaches.

Visual Intelligence: Machines and Minds

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

Vision-Based Quadrotor Navigation

Discusses quadrotor navigation using deep reinforcement learning and low-level control, focusing on visual intelligence and gaze model robustness.

Deep Learning: Convolutional Neural Networks and Training Techniques

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

Delineation: Techniques and Applications

Explores techniques for delineation, including Hough transform, gradient orientation, and shape detection, emphasizing the importance of combining graph-based techniques and machine learning.

Autonomous Vehicles: Intelligence and Perception

Explores intelligence, perception, and AI applications in autonomous vehicles, emphasizing rational thinking and social intelligence.