Computer Vision Architecture Evolution: ConvNets to Transformers - Lecture 21
Explore the evolution of vision architectures, comparing SWIN and CNNs, and learn about modernizing ConvNets with macro and micro design changes for improved performance.
Rethinking and Improving Relative Position Encoding for Vision Transformer - Lecture 23
Explore vision transformer improvements through relative position encoding, examining bias modes, piecewise indexing, and 2D calculations for enhanced performance in image processing tasks.
Emerging Properties in Self-Supervised Vision Transformers - Lecture 25
Explore emerging properties in self-supervised vision transformers, covering related topics, model overview, and implementation details for advanced computer vision applications.
Human Activity Recognition - Learning with Less Labels and Privacy Preservation
Learn advanced techniques for human activity recognition with limited labeled data and privacy preservation, including pseudoleveling, contrastive learning, and zero-shot approaches.
Contextual Image Understanding and Scene Identification in Videos - Thesis Defense
Explore image understanding using context, focusing on scene identification and object detection in videos through innovative techniques like Top-Down Visual Tree representation and Random Walk incorporation.
Learning to Navigate in Cities Without a Map
Explore reinforcement learning techniques for autonomous navigation in unmapped urban environments, focusing on value functions, courier tasks, and multi-site experiments.
Computer Vision - Adversarial Attacks and Deepfool Algorithm - Lecture 1
Explore DeepFool algorithm for generating adversarial examples in deep neural networks, covering linearization, multiclass classification, and experimental results.
Instance Segmentation - Lecture 26
Explore instance segmentation in computer vision, covering Mask R-CNN, ROI pooling, and alignment techniques for precise object detection and pixel-level classification.
Exploring Relationships Between Ground and Aerial Views by Synthesis and Matching
Explore relationships between ground and aerial views through synthesis and matching techniques, focusing on architecture, evaluation methods, and practical applications in image retrieval and GPS data.
Image Segmentation Basics and Techniques - Lecture 23
Explore image segmentation basics, including binarization, thresholding, and region-based techniques. Learn algorithms, visualization methods, and practical applications in computer vision.
Cross-View Geo-Localization - Ground-to-Aerial Image Matching
Explore cross-view geo-localization techniques, focusing on ground-to-aerial image matching. Learn about approaches, limitations, and advancements in this field of computer vision.
Object Detection in Computer Vision - Lecture 22
Explore advanced object detection techniques in computer vision, including R-CNN, Fast R-CNN, and Faster R-CNN. Learn about training processes, region proposals, and improvements in detection algorithms.
Object Detection in Computer Vision - Lecture 21
Explore advanced object detection techniques, including precision-recall metrics, mean average precision, and multitask networks for computer vision applications.
Object Detection Techniques - Part I - Lecture 20
Explore object detection techniques including sliding windows, pyramid construction, feature extraction, and post-processing. Learn about evaluation metrics like IoU, precision, and recall for assessing detection accuracy.
Classification in Computer Vision - Part II - Lecture 19
Explore advanced classification techniques in computer vision, including SVMs, neural networks, and fully convolutional networks. Learn about activation functions, loss functions, and optimization for effective model training.
Classification in Computer Vision - Lecture 18
Explore classification in computer vision, covering features, linear classification, nearest neighbor, decision boundaries, and maximum margin classifiers. Gain insights into fundamental algorithms and problem-solving approaches.
Features in Computer Vision - Lecture 14
Explore feature types, uses, and characteristics in computer vision, including matching, structure from motion, and panorama stitching. Learn about interest points and corner detection for keypoint applications.
Training Neural Networks - Part II - Lecture 11
Explore advanced neural network training techniques, including loss functions, learning rates, momentum, and strategies to prevent overfitting for improved computer vision model performance.
Training Neural Networks for Computer Vision - Part I - Lecture 10
Explore neural network training techniques, including gradient descent, loss functions, and backpropagation, for effective computer vision model optimization and performance improvement.
Introduction to Convolutional Neural Networks - Part I - Lecture 8
Introduction to convolutional neural networks, covering architecture, applications in computer vision and natural language processing, with examples like AlexNet and super-resolution techniques.
Introduction to Neural Networks - Part II - Lecture 8
Explore neural network fundamentals, including activation functions, multi-class classification, and multi-layer perceptrons. Learn about their implementation and performance on image datasets like MNIST.