LVFace: Large Vision model for Face Recogniton
원문 초록
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).
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
GhostFaceNets: Lightweight Face Recognition Model From Cheap Operations
Differentiable Nonlinear Model Predictive Control
Making Language Model a Hierarchical Classifier and Generator
Attributes Shape the Embedding Space of Face Recognition Models
Implementing Adaptations for Vision AutoRegressive Model
Graph World Model
토론
이 논문과 연결된 포럼 글이 아직 없습니다.