Despite progress, deep neural networks still suffer performance declines
under distribution shifts between training and test domains, leading to a
substantial decrease in Quality of Experience (QoE) for multimedia
applications. Existing test-time adaptation (TTA) methods are challenged by
dynamic, multiple test distributions within batches. This work provides a new
perspective on analyzing batch normalization techniques through class-related
and class-irrelevant features, our observations reveal combining source and
test batch normalization statistics robustly characterizes target
distributions. However, test statistics must have high similarity. We thus
propose Discover Your Neighbours (DYN), the first backward-free approach
specialized for dynamic TTA. The core innovation is identifying similar samples
via instance normalization statistics and clustering into groups which provides
consistent class-irrelevant representations. Specifically, Our DYN consists of
layer-wise instance statistics clustering (LISC) and cluster-aware batch
normalization (CABN). In LISC, we perform layer-wise clustering of approximate
feature samples at each BN layer by calculating the cosine similarity of
instance normalization statistics across the batch. CABN then aggregates SBN
and TCN statistics to collaboratively characterize the target distribution,
enabling more robust representations. Experimental results validate DYN's
robustness and effectiveness, demonstrating maintained performance under
dynamic data stream patterns.

本文通过分析批量归一化技术中与类相关和与类无关的特征，将源域和测试域的统计数据进行组合，从而鲁棒地表征目标分布，并提出了针对动态测试时间适应的第一种无需反向传播的方法 DYN，该方法通过实例归一化统计量鉴别相似样本并进行聚类，以提供一致的与类无关表示。实验结果验证了 DYN 的鲁棒性和有效性，并展示了在动态数据流模式下维持性能的能力。