Test-time adaptation (TTA) aims to adapt a model, initially trained on training data, to potential distribution shifts in the test data. Most existing TTA studies, however, focus on classification tasks, leaving a notable gap in the exploration of TTA for semantic segmentation. This pronounced emphasis on classification might lead numerous newcomers and engineers to mistakenly assume that classic TTA methods designed for classification can be directly applied to segmentation. Nonetheless, this assumption remains unverified, posing an open question. To address this, we conduct a systematic, empirical study to disclose the unique challenges of segmentation TTA, and to determine whether classic TTA strategies can effectively address this task. Our comprehensive results have led to three key observations. First, the classic batch norm updating strategy, commonly used in classification TTA, only brings slight performance improvement, and in some cases it might even adversely affect the results. Even with the application of advanced distribution estimation techniques like batch renormalization, the problem remains unresolved. Second, the teacher-student scheme does enhance training stability for segmentation TTA in the presence of noisy pseudo-labels. However, it cannot directly result in performance improvement compared to the original model without TTA. Third, segmentation TTA suffers a severe long-tailed imbalance problem, which is substantially more complex than that in TTA for classification. This long-tailed challenge significantly affects segmentation TTA performance, even when the accuracy of pseudo-labels is high. In light of these observations, we conclude that TTA for segmentation presents significant challenges, and simply using classic TTA methods cannot address this problem well.

翻译：测试时自适应（TTA）旨在使初始在训练数据上训练的模型适应测试数据中可能存在的分布偏移。然而，现有大多数TTA研究聚焦于分类任务，在语义分割的TTA探索方面存在显著空白。这种对分类任务的过度强调可能导致许多新手和工程师错误地认为，专为分类设计的经典TTA方法可直接应用于分割任务。然而，这一假设尚未得到验证，仍是一个悬而未决的问题。为此，我们通过系统性的实证研究揭示了分割TTA的特殊挑战，并验证经典TTA策略能否有效解决该任务。综合实验得出三项关键发现：第一，分类TTA中常用的经典批归一化更新策略仅能带来轻微性能提升，甚至在某些情况下会损害结果——即便采用批重归一化等高级分布估计技术，该问题仍未解决。第二，教师-学生方案确实能增强存在噪声伪标签的分割TTA训练稳定性，但相较于未使用TTA的原始模型，该方案无法直接带来性能提升。第三，分割TTA面临比分类TTA更复杂的严重长尾不平衡问题——即便伪标签准确度较高时，该挑战仍显著影响分割TTA性能。基于这些发现，我们得出结论：分割TTA面临重大挑战，简单沿用经典TTA方法无法有效解决该问题。