Publications de Mohammad Saeed Rad

Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation

Jean-Philippe Thiran, Mohammad Saeed Rad, Guillaume Marc Georges Vray, Seyedbehzad Bozorgtabar, Devavrat Tomar

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 the cohort's images is feasible, annotating all available images is impracti ...

IEEE COMPUTER SOC2022

Benefiting From Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution

Jean-Philippe Thiran, Mohammad Saeed Rad, Claudiu-Cristian Musat, Hazim Kemal Ekenel, Seyedbehzad Bozorgtabar, Thomas Yu

Super-resolution (SR) has traditionally been based on pairs of high-resolution images (HR) and their low-resolution (LR) counterparts obtained artificially with bicubic downsampling. However, in real-world SR, there is a large variety of realistic image de ...

2021

Context-Aware Image Super-Resolution Using Deep Neural Networks

Mohammad Saeed Rad

Image super-resolution is a classic ill-posed computer vision and image processing problem, addressing the question of how to reconstruct a high-resolution image from its low-resolution counterpart. Current state-of-the-art methods have improved the perfor ...

EPFL2021

Using Dynamic Features for Automatic Cervical Precancer Detection

Jean-Philippe Thiran, Mohammad Saeed Rad, Roser Vinals Terres

Cervical cancer remains a major public health concern in developing countries due to financial and human resource constraints. Visual inspection with acetic acid (VIA) of the cervix was widely promoted and routinely used as a low-cost primary screening tes ...

2021

Beekeeping - artificial neural networks to fight varroa

Jean-Philippe Thiran, Mohammad Saeed Rad, Christian Robert Abbet, Maxime Camille Bohnenblust, Roser Vinals Terres, Alain Bugnon

AGRARFORSCHUNG2021

Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images

Jean-Philippe Thiran, Mohammad Saeed Rad, Seyedbehzad Bozorgtabar, Thomas Yu

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 low-resolution (LR) inputs at testing. As the RF images should be similar i ...

IEEE COMPUTER SOC2021

Benefiting from Multitask Learning to Improve Single Image Super-Resolution

Jean-Philippe Thiran, Mohammad Saeed Rad, Claudiu-Cristian Musat, Hazim Kemal Ekenel

Despite significant progress toward super resolving more realistic images by deeper convolutional neural networks (CNNs), reconstructing fine and natural textures still remains a challenging problem. Recent works on single image super resolution (SISR) are ...

2020

Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis

Jean-Philippe Thiran, Mohammad Saeed Rad, Hazim Kemal Ekenel

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 expression recognition accuracy despite having small number of real training images. ...

IEEE2019

Learn to synthesize and synthesize to learn

Jean-Philippe Thiran, Mohammad Saeed Rad, Hazim Kemal Ekenel, Seyedbehzad Bozorgtabar

Attribute guided face image synthesis aims to manipulate attributes on a face image. Most existing methods for image-to-image translation can either perform a fixed translation between any two image domains using a single attribute or require training data ...

2019

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution

Jean-Philippe Thiran, Mohammad Saeed Rad, Hazim Kemal Ekenel

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 low-resolution counterpart. Although such objective functions generate near-ph ...

IEEE COMPUTER SOC2019

SynDeMo: Synergistic Deep Feature Alignment for Joint Learning of Depth and Ego-Motion

Jean-Philippe Thiran, Mohammad Saeed Rad, Seyedbehzad Bozorgtabar

Despite well-established baselines, learning of scene depth and ego-motion from monocular video remains an ongoing challenge, specifically when handling scaling ambiguity issues and depth inconsistencies in image sequences. Much prior work uses either a su ...

IEEE COMPUTER SOC2019

A Computer Vision System to Localize and Classify Wastes on the Streets

Jean-Philippe Thiran, Andreas von Kaenel, Mohammad Saeed Rad, André Droux, Hazim Kemal Ekenel

Littering quantification is an important step for improving cleanliness of cities. When human interpretation is too cumbersome or in some cases impossible, an objective index of cleanliness could reduce the littering by awareness actions. In this paper, we ...

Springer International Publishing2017