activity
20182025
most citedReconstructing Interpretable Features in Computational Super-Resolution microscopy via Regularized Latent Search

1 citations · 1 across the 7 of their papers we have counts for

collaborators

9 papers

cs.LG2025

The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results

Qiuyu Chen, Xin Jin, Yue Song +45

This paper reviews the 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real), held in conjunction with ICCV 2025. The workshop a…

cs.CV2025

DiViD: Disentangled Video Diffusion for Static-Dynamic Factorization

Marzieh Gheisari, Auguste Genovesio

Unsupervised disentanglement of static appearance and dynamic motion in video remains a fundamental challenge, often hindered by information leakage and blurry reconstructions in e…

eess.IV2024★ 1 cited

Reconstructing Interpretable Features in Computational Super-Resolution microscopy via Regularized Latent Search

Marzieh Gheisari, Auguste Genovesio

Supervised deep learning approaches can artificially increase the resolution of microscopy images by learning a mapping between two image resolutions or modalities. However, such m…

cs.CV2023

Super-Resolution through StyleGAN Regularized Latent Search: A Realism-Fidelity Trade-off

Marzieh Gheisari, Auguste Genovesio

This paper addresses the problem of super-resolution: constructing a highly resolved (HR) image from a low resolved (LR) one. Recent unsupervised approaches search the latent space…

cs.CV2022

AggNet: Learning to Aggregate Faces for Group Membership Verification

Marzieh Gheisari, Javad Amirian, Teddy Furon +1

In some face recognition applications, we are interested to verify whether an individual is a member of a group, without revealing their identity. Some existing methods, propose a…

cs.CV2020

Joint Learning of Assignment and Representation for Biometric Group Membership

Marzieh Gheisari, Teddy Furon, Laurent Amsaleg

This paper proposes a framework for group membership protocols preventing the curious but honest server from reconstructing the enrolled biometric signatures and inferring the iden…