让模型在推理时利用时间顺序自动调整，提升持续输入下的准确率。
Order-Aware Test-Time Adaptation: Leveraging Temporal Dynamics for Robust Streaming Inference
- 基于时间动态建模，用递归贝叶斯估计更新预测结果。
- 在多类任务中提升准确率，最高达6.35%的改进。
- 适合流式数据场景，对轻量级模型友好，无需额外训练。
测试时自适应（TTA）使预训练模型能通过未标注的测试流进行调整以应对分布偏移。然而现有方法通常将这些流视为独立样本，忽略了时间动态中蕴含的监督信号。为此，我们提出有序感知测试时自适应（OATTA）。将测试时自适应建模为无梯度的递归贝叶斯估计问题，利用学习得到的动态转移矩阵作为时间先验，优化基础模型的预测。为确保在弱结构化流中的安全性，引入似然比门控机制（LLR），当缺乏时间证据时回退至基础预测器。OATTA是一个轻量、模型无关模块，计算开销极低。跨图像分类、可穿戴与生理信号分析、语言情感分析等任务的广泛实验表明其通用性；OATTA始终提升现有基线性能，准确率最高提升6.35%。研究发现，建模时间动态提供了一种超越传统无序感知TTA的关键互补信号。
原文摘要 · Abstract (English)
Test-Time Adaptation (TTA) enables pre-trained models to adjust to distribution shift by learning from unlabeled test-time streams. However, existing methods typically treat these streams as independent samples, overlooking the supervisory signal inherent in temporal dynamics. To address this, we introduce Order-Aware Test-Time Adaptation (OATTA). We formulate test-time adaptation as a gradient-free recursive Bayesian estimation task, using a learned dynamic transition matrix as a temporal prior to refine the base model's predictions. To ensure safety in weakly structured streams, we introduce a likelihood-ratio gate (LLR) that reverts to the base predictor when temporal evidence is absent. OATTA is a lightweight, model-agnostic module that incurs negligible computational overhead. Extensive experiments across image classification, wearable and physiological signal analysis, and language sentiment analysis demonstrate its universality; OATTA consistently boosts established baselines, improving accuracy by up to 6.35%. Our findings establish that modeling temporal dynamics provides a critical, orthogonal signal beyond standard order-agnostic TTA approaches.
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