2 citations · 5 across the 5 of their papers we have counts for
6 papers
VB-Mitigator: An Open-source Framework for Evaluating and Advancing Visual Bias Mitigation
Ioannis Sarridis, Christos Koutlis, Symeon Papadopoulos +1
Bias in computer vision models remains a significant challenge, often resulting in unfair, unreliable, and non-generalizable AI systems. Although research into bias mitigation has…
FaceX: Understanding Face Attribute Classifiers through Summary Model Explanations
Ioannis Sarridis, Christos Koutlis, Symeon Papadopoulos +1
EXplainable Artificial Intelligence (XAI) approaches are widely applied for identifying fairness issues in Artificial Intelligence (AI) systems. However, in the context of facial a…
MAVias: Mitigate any Visual Bias
Ioannis Sarridis, Christos Koutlis, Symeon Papadopoulos +1
Mitigating biases in computer vision models is an essential step towards the trustworthiness of artificial intelligence models. Existing bias mitigation methods focus on a small se…
BAdd: Bias Mitigation through Bias Addition
Ioannis Sarridis, Christos Koutlis, Symeon Papadopoulos +1
Computer vision (CV) datasets often exhibit biases that are perpetuated by deep learning models. While recent efforts aim to mitigate these biases and foster fair representations,…
Sampling Strategies for Mitigating Bias in Face Synthesis Methods
Emmanouil Maragkoudakis, Symeon Papadopoulos, Iraklis Varlamis +1
Synthetically generated images can be used to create media content or to complement datasets for training image analysis models. Several methods have recently been proposed for the…
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Pietro Melzi, Ruben Tolosana, Ruben Vera-Rodriguez +44
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must…