IG-FIQA: Improving Face Image Quality Assessment through Intra-class Variance Guidance robust to Inaccurate Pseudo-Labels
<|reference_start|>IG-FIQA: Improving Face Image Quality Assessment through Intra-class Variance Guidance robust to Inaccurate Pseudo-Labels: In the realm of face image quality assesment (FIQA), method based on sample relative classification have shown impressive performance. However, the quality scores used as pseudo-labels assigned from images of classes with low intra-class variance could be unrelated to the actual quality in this method. To address this issue, we present IG-FIQA, a novel approach to guide FIQA training, introducing a weight parameter to alleviate the adverse impact of these classes. This method involves estimating sample intra-class variance at each iteration during training, ensuring minimal computational overhead and straightforward implementation. Furthermore, this paper proposes an on-the-fly data augmentation methodology for improved generalization performance in FIQA. On various benchmark datasets, our proposed method, IG-FIQA, achieved novel state-of-the-art (SOTA) performance.<|reference_end|>
@article{kim2024ig-fiqa:,
    title={IG-FIQA: Improving Face Image Quality Assessment through Intra-class
  Variance Guidance robust to Inaccurate Pseudo-Labels},
    author={Minsoo Kim, Gi Pyo Nam, Haksub Kim, Haesol Park, and Ig-Jae Kim},
    journal={arXiv preprint arXiv:2403.08256},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08256},
    primaryClass={cs.CV}
}