Radu Timofte

Latest

• Learning Accurate Dense Correspondences and When to Trust Them
• DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows
• Deep Burst Super-Resolution
• The Heterogeneity Hypothesis: Finding Layer-Wise Dissimilated Network Architecture
• Few-Shot Classification By Few-Iteration Meta-Learning
• DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation
• GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network
• How to Train Your Energy-Based Model for Regression
• Learning What to Learn for Video Object Segmentation
• SRFlow: Learning the Super-Resolution Space with Normalizing Flow
• Know Your Surroundings: Exploiting Scene Information for Object Tracking
• GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences
• Probabilistic Regression for Visual Tracking
• Learning Discriminative Model Prediction for Tracking
• Tracking the Known and the Unknown by Leveraging Semantic Information