Generative Models for Image Synthesis
Explore cutting-edge generative models for image synthesis, translation, and defect detection. Learn about GANs, unsupervised techniques, and interactive applications in this advanced AI seminar.
Regularization of Big Neural Networks
Explore regularization techniques for large neural networks, including DropOut, DropConnect, and stochastic pooling. Learn about deconvolutional networks and their applications in computer vision tasks.
Filtering in Computer Vision - Lecture 2
Learn about image filtering techniques, including noise reduction, edge detection, and smoothing. Explore applications in computer vision and gain hands-on experience with MATLAB functions for image processing.
CAP5415 - Digital Image Processing: Filtering and Noise Reduction - Lecture 3
Explore image processing fundamentals, including digitization, histograms, noise types, and filtering techniques. Learn about derivatives, averages, and correlation in the context of digital image analysis.
Unsupervised Video Interpolation Using Cycle Consistency
Explore cutting-edge unsupervised video interpolation techniques using cycle consistency, including implementation, constraints, and experimental results for improved frame generation.
Extreme View Synthesis: Novel Techniques and Challenges
Explore advanced techniques for synthesizing novel views from limited input images, covering multiplane images, patch matching, and depth probability estimation.
Subspace Capsule Network
Explore Subspace Capsule Networks: a novel approach to neural networks that preserves information, norms, and angles. Learn about its principles, applications, and advantages in image classification and generation.
Deep Constrained Dominant Sets for Person Re-Identification
Explore advanced techniques for person re-identification using deep learning, including triplet loss, quadruplet loss, diffusion-based methods, and constrained dominant sets.
Capsule Networks for Computer Vision – CVPR 2019 Tutorial
Explore capsule networks for computer vision, covering architecture, challenges, applications, and advancements in this cutting-edge deep learning approach.
Capsule Networks - A Survey by Dr. Yogesh Rawat, UCF
Explore capsule networks in computer vision, covering dynamic routing, equivariance, and applications in segmentation, localization, and medical imaging.
Generalization to Video Capsules - From Convolutional to Video Capsule Networks
Explore video capsule networks for action detection and object segmentation, including multi-modal approaches and semi-supervised techniques for improved performance in computer vision tasks.
Learning to See in the Dark - Extreme Low Light Imaging Techniques
Explore extreme low-light imaging techniques using deep learning, covering dataset creation, network architecture, and experimental results for improved night vision photography.
Describing Images Using Semantic Modeling with Attributes and Tags
Explore semantic modeling for image description using attributes and tags, covering frameworks, methodologies, and experiments in visual content analysis and popularity prediction.
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Explore fine-grained text-to-image generation using attentional GANs, covering architecture, experiments, and novel scenarios in this technical deep dive.
Video Action Transformer Network: Implementation and Experiments
Learn about Video Action Transformer Networks, exploring their architecture, implementation, and experimental results for advanced video understanding and action recognition.
Deep Network for Integrated 3D Sensing of Multiple People in Natural Images
Explore advanced techniques for 3D human pose estimation and shape reconstruction in natural images using deep learning and computer vision approaches.
VideoCapsuleNet - A Simplified Network for Action Detection
Explore VideoCapsuleNet, a simplified network for action detection in videos. Learn about capsule networks, convolutional layers, and innovative techniques for improved video analysis.
Improving Transferability of Adversarial Examples with Input Diversity - CAP6412 Spring 2021
Explore techniques to enhance adversarial example transferability using input diversity, including FGSM family methods and ensemble network attacks.
Explaining and Harnessing Adversarial Examples in Machine Learning - Spring 2021
Explore adversarial examples in machine learning, their impact on linear and non-linear models, and techniques like adversarial training to improve model robustness and generalization.
CAP6412 - SmoothGrad: Removing Noise by Adding Noise - Lecture
Explore SmoothGrad, a technique for enhancing neural network visualization by adding noise to input images and averaging resulting sensitivity maps.
Unmasking Clever Hans Predictors and Assessing Machine Learning - Spring 2021
Unmasking clever predictors: Explore Layer-wise Relevance Propagation and SPRAY techniques to assess machine learning models' decision-making processes and identify potential biases or faults.