32 citations · 117 across the 14 of their papers we have counts for
16 papers
Unpaired Translation from Semantic Label Maps to Images by Leveraging Domain-Specific Simulations
Lin Zhang, Tiziano Portenier, Orcun Goksel
Photorealistic image generation from simulated label maps are necessitated in several contexts, such as for medical training in virtual reality. With conventional deep learning met…
Generative Feature-driven Image Replay for Continual Learning
Kevin Thandiackal, Tiziano Portenier, Andrea Giovannini +2
Neural networks are prone to catastrophic forgetting when trained incrementally on different tasks. Popular incremental learning methods mitigate such forgetting by retaining a sub…
Content-Preserving Unpaired Translation from Simulated to Realistic Ultrasound Images
Devavrat Tomar, Lin Zhang, Tiziano Portenier +1
Interactive simulation of ultrasound imaging greatly facilitates sonography training. Although ray-tracing based methods have shown promising results, obtaining realistic images re…
Probabilistic Spatial Analysis in Quantitative Microscopy with Uncertainty-Aware Cell Detection using Deep Bayesian Regression of Density Maps
Alvaro Gomariz, Tiziano Portenier, César Nombela-Arrieta +1
3D microscopy is key in the investigation of diverse biological systems, and the ever increasing availability of large datasets demands automatic cell identification methods that n…
Utilizing Uncertainty Estimation in Deep Learning Segmentation of Fluorescence Microscopy Images with Missing Markers
Alvaro Gomariz, Raphael Egli, Tiziano Portenier +2
Fluorescence microscopy images contain several channels, each indicating a marker staining the sample. Since many different marker combinations are utilized in practice, it has bee…
Learning Ultrasound Rendering from Cross-Sectional Model Slices for Simulated Training
Lin Zhang, Tiziano Portenier, Orcun Goksel
Purpose. Given the high level of expertise required for navigation and interpretation of ultrasound images, computational simulations can facilitate the training of such skills in…
Modality Attention and Sampling Enables Deep Learning with Heterogeneous Marker Combinations in Fluorescence Microscopy
Alvaro Gomariz, Tiziano Portenier, Patrick M. Helbling +4
Fluorescence microscopy allows for a detailed inspection of cells, cellular networks, and anatomical landmarks by staining with a variety of carefully-selected markers visualized a…
GramGAN: Deep 3D Texture Synthesis From 2D Exemplars
Tiziano Portenier, Siavash Bigdeli, Orcun Goksel
We present a novel texture synthesis framework, enabling the generation of infinite, high-quality 3D textures given a 2D exemplar image. Inspired by recent advances in natural text…
Deep Image Translation for Enhancing Simulated Ultrasound Images
Lin Zhang, Tiziano Portenier, Christoph Paulus +1
Ultrasound simulation based on ray tracing enables the synthesis of highly realistic images. It can provide an interactive environment for training sonographers as an educational t…
Learning Generative Models using Denoising Density Estimators
Siavash A. Bigdeli, Geng Lin, Tiziano Portenier +2
Learning probabilistic models that can estimate the density of a given set of samples, and generate samples from that density, is one of the fundamental challenges in unsupervised…
Smart, Deep Copy-Paste
Tiziano Portenier, Qiyang Hu, Paolo Favaro +1
In this work, we propose a novel system for smart copy-paste, enabling the synthesis of high-quality results given a masked source image content and a target image context as input…
Learning to Take Directions One Step at a Time
Qiyang Hu, Adrian Wälchli, Tiziano Portenier +2
We present a method to generate a video sequence given a single image. Because items in an image can be animated in arbitrarily many different ways, we introduce as control signal…
Specular-to-Diffuse Translation for Multi-View Reconstruction
Shihao Wu, Hui Huang, Tiziano Portenier +4
Most multi-view 3D reconstruction algorithms, especially when shape-from-shading cues are used, assume that object appearance is predominantly diffuse. To alleviate this restrictio…
FaceShop: Deep Sketch-based Face Image Editing
Tiziano Portenier, Qiyang Hu, Attila Szabó +3
We present a novel system for sketch-based face image editing, enabling users to edit images intuitively by sketching a few strokes on a region of interest. Our interface features…
Challenges in Disentangling Independent Factors of Variation
Attila Szabó, Qiyang Hu, Tiziano Portenier +2
We study the problem of building models that disentangle independent factors of variation. Such models could be used to encode features that can efficiently be used for classificat…
Disentangling Factors of Variation by Mixing Them
Qiyang Hu, Attila Szabó, Tiziano Portenier +2
We propose an approach to learn image representations that consist of disentangled factors of variation without exploiting any manual labeling or data domain knowledge. A factor of…