347 citations · 1.3k across the 70 of their papers we have counts for
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Revisiting Few-Shot Learning for Facial Expression Recognition
Anca-Nicoleta Ciubotaru, Arnout Devos, Behzad Bozorgtabar +2
Most of the existing deep neural nets on automatic facial expression recognition focus on a set of predefined emotion classes, where the amount of training data has the biggest imp…
SROBB: Targeted Perceptual Loss for Single Image Super-Resolution
Mohammad Saeed Rad, Behzad Bozorgtabar, Urs-Viktor Marti +3
By benefiting from perceptual losses, recent studies have improved significantly the performance of the super-resolution task, where a high-resolution image is resolved from its lo…
Robust Monte-Carlo Simulations in Diffusion-MRI: Effect of the substrate complexity and parameter choice on the reproducibility of results
Jonathan Rafael-Patino, David Romascano, Alonso Ramirez-Manzanares +3
Monte-Carlo Diffusion Simulations (MCDS) have been used extensively as a ground truth tool for the validation of microstructure models for Diffusion-Weighted MRI. However, methodol…
Benefiting from Multitask Learning to Improve Single Image Super-Resolution
Mohammad Saeed Rad, Behzad Bozorgtabar, Claudiu Musat +4
Despite significant progress toward super resolving more realistic images by deeper convolutional neural networks (CNNs), reconstructing fine and natural textures still remains a c…
Exploring Factors for Improving Low Resolution Face Recognition
Omid Abdollahi Aghdam, Behzad Bozorgtabar, Hazım Kemal Ekenel +1
State-of-the-art deep face recognition approaches report near perfect performance on popular benchmarks, e.g., Labeled Faces in the Wild. However, their performance deteriorates si…
Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis
Behzad Bozorgtabar, Mohammad Saeed Rad, Hazim Kemal Ekenel +1
Cross-domain synthesizing realistic faces to learn deep models has attracted increasing attention for facial expression analysis as it helps to improve the performance of expressio…