CVPR 2023 - Instance-Aware Domain Generalization for Face Anti-Spoofing

In this episode we discuss Instance-Aware Domain Generalization for Face Anti-Spoofing by Qianyu Zhou, Ke-Yue Zhang, Taiping Yao, Xuequan Lu, Ran Yi, Shouhong Ding, Lizhuang Ma. The paper discusses the development of a Face Anti-Spoofing (FAS) system based on Domain Generalization (DG) which aligns features on the instance level without relying on domain labels. This is in contrast to previous methods that focused on domain-level alignment and used artificial domain labels that did not accurately reflect real domain distributions. The proposed Instance-Aware Domain Generalization framework utilizes Asymmetric Instance Adaptive Whitening, Dynamic Kernel Generator, and Categorical Style Assembly to improve generalization and eliminate style-sensitive feature correlation. The paper concludes that their method outperforms state-of-the-art competitors.