Since distribution shifts are likely to occur during test-time and can
drastically decrease the model's performance, online test-time adaptation (TTA)
continues to update the model after deployment, leveraging the current test
data. Clearly, a method proposed for online TTA has to perform well for all
kinds of environmental conditions. By introducing the variable factors 'domain
non-stationarity' and 'temporal correlation', we first unfold all practically
relevant settings and define the entity as universal TTA. To tackle the problem
of universal TTA, we identify and highlight several challenges a self-training
based method has to deal with, including: 1) model bias and the occurrence of
trivial solutions when performing entropy minimization on varying sequence
lengths with and without multiple domain shifts, 2) loss of generalization
which exacerbates the adaptation to future domain shifts and the occurrence of
catastrophic forgetting, and 3) performance degradation due to shifts in label
prior. To prevent the model from becoming biased, we leverage a dataset and
model-agnostic certainty and diversity weighting. In order to maintain
generalization and prevent catastrophic forgetting, we propose to continually
weight-average the source and adapted model. To compensate for disparities in
the label prior during test-time, we propose an adaptive additive prior
correction scheme. We evaluate our approach, named ROID, on a wide range of
settings, datasets, and models, setting new standards in the field of universal
TTA.

本文提出了一种名为 ROID 的方法，该方法通过使用确定性和多样性加权、源模型和自适应模型的加权平均以及自适应添加先验校正方案等多项技术手段，解决了在线测试时自适应的问题，以及由于多种因素引起的模型性能下降问题。该方法在多种数据集、模型和场景下进行了评估，并在普遍 TTA 领域取得了新的标准。