  Vision Transformer (ViT) is becoming more popular in image processing.
Specifically, we investigate the effectiveness of test-time adaptation (TTA) on
ViT, a technique that has emerged to correct its prediction during test-time by
itself. First, we benchmark various test-time adaptation approaches on ViT-B16
and ViT-L16. It is shown that the TTA is effective on ViT and the
prior-convention (sensibly selecting modulation parameters) is not necessary
when using proper loss function. Based on the observation, we propose a new
test-time adaptation method called class-conditional feature alignment (CFA),
which minimizes both the class-conditional distribution differences and the
whole distribution differences of the hidden representation between the source
and target in an online manner. Experiments of image classification tasks on
common corruption (CIFAR-10-C, CIFAR-100-C, and ImageNet-C) and domain
adaptation (digits datasets and ImageNet-Sketch) show that CFA stably
outperforms the existing baselines on various datasets. We also verify that CFA
is model agnostic by experimenting on ResNet, MLP-Mixer, and several ViT
variants (ViT-AugReg, DeiT, and BeiT). Using BeiT backbone, CFA achieves 19.8%
top-1 error rate on ImageNet-C, outperforming the existing test-time adaptation
baseline 44.0%. This is a state-of-the-art result among TTA methods that do not
need to alter training phase.
