62 citations · 99 across the 32 of their papers we have counts for
8 papers · 2 filters
GRACE: Adaptive Concept Erasure with Geometry-Guided Retention in Diffusion Models
Qinghui Gong, Yihuai Liang, Yuanlun Xie +3
Text-to-image (T2I) diffusion models inevitably internalize sensitive or non-compliant concepts from large-scale pretraining data, necessitating post-hoc concept erasure. However,…
SCOUT: Semantic Concept Discovery for Open-Vocabulary Editing of face Recognition Templates
Leon Todorov, Peter Rot, Peter Peer +2
Face recognition templates are compact identity representations, yet they also encode rich semantic information about facial appearance. Prior work has shown that templates can be…
IJCB-AFMFR 2026: Competition on Adapting Foundation Models for Face Recognition Using Synthetic Training Data
Tahar Chettaoui, Guray Ozgur, Eduarda Caldeira +13
This paper presents a summary of the Competition on Adapting Foundation Models for Face Recognition Using Synthetic Training Data (AFMFR), held at the 2026 International Joint Conf…
Beyond Error-vs-Discard Characteristic: Toward Stable and Reliable Evaluation for Face Image Quality Assessment
Bhavesh Wani, Žiga Babnik, Vitomir Štruc +1
Face Image Quality Assessment (FIQA) aims to estimate the utility of facial images for reliable recognition. The evaluation of FIQA methods is predominantly based on the Error-vers…
Employing Vision-Language Models for Face Image Quality Assessment
Erdi Sarıtaş, Eren Onaran, Vitomir Štruc +1
Face Image Quality Assessment (FIQA) is a crucial control step in biometric pipelines. It ensures only reliable samples are processed to maintain system accuracy. State-of-the-art…
PreFIQs: Face Image Quality Is What Survives Pruning
Jan Niklas Kolf, Guray Ozgur, Andrea Atzori +4
Face Image Quality Assessment (FIQA) evaluates the utility of a face image for automated face recognition (FR) systems. In this work, we propose PreFIQs, an unsupervised and traini…