{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/lvface-large-vision-model-for-face-recogniton","title":"LVFace: Large Vision model for Face Recogniton","arxiv_id":"2501.13420","date":"2025-01-23","proceeding":null,"authors":["Jinghan You","Yuanrui Sun","Mingyu Guo","Chao Feng","Jiao Ran"],"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. 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