While recent face anti-spoofing methods perform well under the intra-domain
setups, an effective approach needs to account for much larger appearance
variations of images acquired in complex scenes with different sensors for
robust performance. In this paper, we present adaptive vision transformers
(ViT) for robust cross-domain face anti-spoofing. Specifically, we adopt ViT as
a backbone to exploit its strength to account for long-range dependencies among
pixels. We further introduce the ensemble adapters module and feature-wise
transformation layers in the ViT to adapt to different domains for robust
performance with a few samples. Experiments on several benchmark datasets show
that the proposed models achieve both robust and competitive performance
against the state-of-the-art methods.

本文提出一种自适应视觉变换器 (ViT) 方法，利用集成适配器模块和特征转换层适应不同域的数据，从而实现跨域活体检测，并在多个基准数据集上取得了与现有方法相媲美的性能.

自适应 Transformers 用于稳健的少样本跨域人脸防伪

Adaptive Transformers for Robust Few-shot Cross-domain Face  Anti-spoofing

Few-shot classification aims to recognize novel categories with only few
labeled images in each class. Existing metric-based few-shot classification
algorithms predict categories by comparing the feature embeddings of query
images with those from a few labeled images (support examples) using a learned
metric function. While promising performance has been demonstrated, these
methods often fail to generalize to unseen domains due to large discrepancy of
the feature distribution across domains. In this work, we address the problem
of few-shot classification under domain shifts for metric-based methods. Our
core idea is to use feature-wise transformation layers for augmenting the image
features using affine transforms to simulate various feature distributions
under different domains in the training stage. To capture variations of the
feature distributions under different domains, we further apply a
learning-to-learn approach to search for the hyper-parameters of the
feature-wise transformation layers. We conduct extensive experiments and
ablation studies under the domain generalization setting using five few-shot
classification datasets: mini-ImageNet, CUB, Cars, Places, and Plantae.
Experimental results demonstrate that the proposed feature-wise transformation
layer is applicable to various metric-based models, and provides consistent
improvements on the few-shot classification performance under domain shift.

本文旨在解决度量方法下少样本分类领域差异的问题，使用特征转换层进行数据增强以模拟不同域下的特征分布，并应用 “学习学习” 方法搜索超参数来捕捉不同域下的特征分布变化，实验结果表明该方法适用于不同的度量模型，能够提高少样本分类准确性。