论文标题
统一的亲属验证方法
A Unified Approach to Kinship Verification
论文作者
论文摘要
在这项工作中，我们使用统一的多任务学习计划提出了一种基于深度学习的方法，以共同学习所有亲属关系。这使我们能够更好地利用典型的亲属验证的小型训练集。我们介绍了一种新颖的方法来融合亲属图像的嵌入，以避免过度拟合，这在训练此类网络中是一个普遍的问题。为训练集图像得出了一种自适应采样方案，以解决亲属验证数据集中固有的不平衡。一项彻底的消融研究例证了我们方法的有效性，当将其应用于野外，FG2018和FG2020数据集中时，实验证明的结果优于当代最先进的亲属验证结果。
In this work, we propose a deep learning-based approach for kin verification using a unified multi-task learning scheme where all kinship classes are jointly learned. This allows us to better utilize small training sets that are typical of kin verification. We introduce a novel approach for fusing the embeddings of kin images, to avoid overfitting, which is a common issue in training such networks. An adaptive sampling scheme is derived for the training set images to resolve the inherent imbalance in kin verification datasets. A thorough ablation study exemplifies the effectivity of our approach, which is experimentally shown to outperform contemporary state-of-the-art kin verification results when applied to the Families In the Wild, FG2018, and FG2020 datasets.
