Fusing quality scores from multiple intermediate transformer blocks in ViTs via depth-weighted averaging improves face image quality assessment on benchmarks without retraining or architecture changes.
ISO/IEC 19795-1:2021 Information technology — Biometric performance testing and reporting — Part 1: Principles and framework
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
ATTN-FIQA computes face image quality scores from pre-softmax attention patterns in pre-trained ViT-based FR models using a single forward pass, showing correlation with recognition utility and spatial interpretability.
citing papers explorer
-
EX-FIQA: Leveraging Intermediate Early eXit Representations from Vision Transformers for Face Image Quality Assessment
Fusing quality scores from multiple intermediate transformer blocks in ViTs via depth-weighted averaging improves face image quality assessment on benchmarks without retraining or architecture changes.
-
ATTN-FIQA: Interpretable Attention-based Face Image Quality Assessment with Vision Transformers
ATTN-FIQA computes face image quality scores from pre-softmax attention patterns in pre-trained ViT-based FR models using a single forward pass, showing correlation with recognition utility and spatial interpretability.