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.
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Pith papers citing it
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Face segmentation for background removal systematically impacts both face recognition performance and morphing attack detection in unconstrained scenarios.
citing papers explorer
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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.
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On the Impact of Face Segmentation-Based Background Removal on Recognition and Morphing Attack Detection
Face segmentation for background removal systematically impacts both face recognition performance and morphing attack detection in unconstrained scenarios.