Abstract
Recently, large vision models have demonstrated powerful representation capabilities in the field of computer vision. However, we unexpectedly found that face recognition research is still mainly focused on CNN-based model architectures, which may lead to suboptimal state-of-the-art (SOTA) performance in face recognition. Therefore, we study how to use various loss functions from historical research orthogonally to train a new state-of-the-art face recognition model based on large vision models, called LVFace. On the largest public face database, WebFace42M, we demonstrated the superiority of LVFace over other advanced face recognition methods and achieved first place in the ICCV21 MFR-Ongoing challenge, until the submission of this work (December 30, 2024, academic track).
Abstract (translated)
最近，大型视觉模型在计算机视觉领域展示了强大的表示能力。然而，我们意外地发现面部识别研究仍然主要集中在基于CNN的模型架构上，这可能导致面部识别领域的最先进（SOTA）性能不佳。因此，我们研究了如何利用历史研究中的各种损失函数来正交训练一种基于大型视觉模型的新一代最先进面部识别模型，称为LVFace。在最大的公共人脸数据库WebFace42M上，我们展示了LVFace优于其他先进的面部识别方法，并在ICCV21 MFR-Ongoing挑战赛中获得第一名（截至提交此项工作时，即2024年12月30日的学术赛道）。
URL
https://arxiv.org/abs/2501.13420
