Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data

arXiv:2404.10378v1 Announce Type: cross
Abstract: Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class variability, time and errors produced in manual labeling, and in some cases privacy concerns, among others. This paper presents an overview of the 2nd edition of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at CVPR 2024. FRCSyn aims to investigate the use …

abstract arxiv challenge class cs.ai cs.cv cs.cy cs.lg cvpr data errors face face recognition labeling machine machine learning machine learning models real data recognition synthetic synthetic data training type