提出动态自适应方法，解决多模态数据在测试时持续退化下的模型遗忘问题。
Analytic Continual Test-Time Adaptation for Multi-Modality Corruption
- 用解析式学习避免灾难性遗忘，实现闭式训练
- 动态融合多模态信息，提升退化场景下可靠性
- 适合应对传感器故障、天气变化等连续退化场景
测试时自适应（TTA）利用无标签测试数据缩小预训练模型在源域与目标域之间的差距，应对测试时因天气变化、噪声或传感器故障导致的分布偏移。多模态连续测试时自适应（MM-CTTA）进一步支持多模态输入并适应持续演化的目标域。然而，现有方法在多模态退化场景下仍面临灾难性遗忘和可靠性偏差等挑战。本文提出多模态动态解析适配器（MDAA），引入解析学习（Analytic Learning）——一种闭式训练技术，通过解析分类器（ACs）缓解遗忘问题；设计动态后融合机制（DLFM），动态选择并整合各模态中的可靠信息。大量实验表明，MDAA在所提任务上达到当前最优性能。
原文摘要 · Abstract (English)
Test-Time Adaptation (TTA) enables pre-trained models to bridge the gap between source and target datasets using unlabeled test data, addressing domain shifts caused by corruptions like weather changes, noise, or sensor malfunctions in test time. Multi-Modal Continual Test-Time Adaptation (MM-CTTA), as an extension of standard TTA, further allows models to handle multi-modal inputs and adapt to continuously evolving target domains. However, MM-CTTA faces critical challenges such as catastrophic forgetting and reliability bias, which are rarely addressed effectively under multi-modal corruption scenarios. In this paper, we propose a novel approach, Multi-modality Dynamic Analytic Adapter (MDAA), to tackle MM-CTTA tasks. MDAA introduces analytic learning,a closed-form training technique,through Analytic Classifiers (ACs) to mitigate catastrophic forgetting. Furthermore, we design the Dynamic Late Fusion Mechanism (DLFM) to dynamically select and integrate reliable information from different modalities. Extensive experiments show that MDAA achieves state-of-the-art performance across the proposed tasks.
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