From the 1 of 8 linked papers with an AI index.
8 papers
Scaling Vision-Language Models Is Not Enough to Mitigate Bias
Ioannis Sarridis, Ioannis Kompatsiaris, Symeon Papadopoulos
The paper empirically evaluates 194 vision‑language models to see how model size, training data, and architecture affect bias, finding that larger models do not consistently reduce…
Face Age Verification Vulnerabilities Under Simple Appearance Manipulations
Ioannis Sarridis, Ioannis Kompatsiaris, Symeon Papadopoulos
Online platforms increasingly rely on automated age estimation systems to enforce minimum-age policies. Focusing on vision-based models designed for this task, concerns arise regar…
Designing and Generating Diverse, Equitable Face Image Datasets for Face Verification Tasks
Georgia Baltsou, Ioannis Sarridis, Christos Koutlis +1
Face verification is a significant component of identity authentication in various applications including online banking and secure access to personal devices. The majority of the…
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…
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…
InDistill: Information flow-preserving knowledge distillation for model compression
Ioannis Sarridis, Christos Koutlis, Giorgos Kordopatis-Zilos +2
In this paper, we introduce InDistill, a method that serves as a warmup stage for enhancing Knowledge Distillation (KD) effectiveness. InDistill focuses on transferring critical in…