80 citations · 80 across the 7 of their papers we have counts for
8 papers
Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation
Devavrat Tomar, Behzad Bozorgtabar, Manana Lortkipanidze +3
In medical image segmentation, supervised deep networks' success comes at the cost of requiring abundant labeled data. While asking domain experts to annotate only one or a few of…
Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images
Mohammad Saeed Rad, Thomas Yu, Behzad Bozorgtabar +1
Existing reference (RF)-based super-resolution (SR) models try to improve perceptual quality in SR under the assumption of the availability of high-resolution RF images paired with…
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution
Mohammad Saeed Rad, Thomas Yu, Claudiu Musat +3
Super-resolution (SR) has traditionally been based on pairs of high-resolution images (HR) and their low-resolution (LR) counterparts obtained artificially with bicubic downsamplin…
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…
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…
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…