Lectures related to Activity recognition

Machine Learning Fundamentals

Covers the fundamental concepts of machine learning, including classification, algorithms, optimization, supervised learning, reinforcement learning, and various tasks like image recognition and text generation.

AI and Ethics

Delves into the potential and ethical challenges of AI technologies, emphasizing responsible research and diverse datasets.

Programming Project: Image Recognition

Explains image recognition in LabVIEW, focusing on reading parameters and recognizing digits.

Thermal Comfort Monitoring: Non-Intrusive Solutions

Presents a framework for non-intrusive thermal comfort monitoring using computer vision and machine learning techniques.

Deep Visual Recognition: Interpretability

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

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.

Fine-Grained Visual Categorization: Challenges and Solutions

Explores challenges and solutions in fine-grained visual categorization, focusing on computer vision and machine learning.

NFNets: Removing BatchNorm for High-Performance Image Recognition

Explores NFNets as an alternative to BatchNorm in ResNets, achieving high performance on ImageNet.

Visual Intelligence: Machines and Minds

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

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, covering image formation, perception, computer vision, correspondence learning, motion analysis, and recognition in videos.

Deep Learning: No Free Lunch Theorem and Inductive Bias

Covers the No Free Lunch Theorem and the role of inductive bias in deep learning and reinforcement learning.

Image Recognition: Datasets and Algorithms

Explores a 2019 paper on image recognition, dataset challenges, biases, and the impact of large-scale datasets on deep learning models.

Visual Intelligence: Machines and Minds

Explores visual intelligence, perception, image classification, and the connection between vision and action in complex systems.

Faster Gaze Prediction: Dense Networks and Fisher Pruning

Explores the development of faster architectures for saliency prediction using deep networks and pruning methods.

Computer Vision Basics: Image Processing and Feature Detection

Covers the basics of computer vision, focusing on image processing techniques and feature detection.

MaxPooling as inductive bias for images

Explores how MaxPooling enforces an inductive bias towards local translation invariance in convolutional neural networks.

Visual Recognition: Integrated 3D + Semantics

Covers visual recognition models, scene graphs, neural networks, and embodied vision.

Topology: Polyhedral Products and Edge Detection

Explores topology concepts and edge detection in computer vision, highlighting the significance of contours and gradients in image analysis.