面向多模态损坏的解析型持续测试时适应
Analytic Continual Test-Time Adaptation for Multi-Modality Corruption
AI总结：
针对多模态持续测试时适应中的灾难性遗忘与可靠性偏差，提出MDAA，用解析分类器和动态后融合机制实现稳定适应，并在相关任务上达到最先进性能。
摘要：
测试时适应（TTA）使预训练模型能够利用未标记测试数据弥合源数据集与目标数据集之间的差距，以应对测试时由天气变化、噪声或传感器故障等损坏引起的域偏移。多模态持续测试时适应（MM-CTTA）作为标准TTA的扩展，进一步使模型能够处理多模态输入并适应持续演变的目标域。然而，MM-CTTA面临灾难性遗忘和可靠性偏差等关键挑战，这些问题在多模态损坏场景下很少得到有效解决。本文提出一种名为多模态动态解析适配器（MDAA）的新方法以处理MM-CTTA任务。MDAA通过解析分类器（AC）引入解析学习这一闭式训练技术，以缓解灾难性遗忘。此外，我们设计了动态后融合机制（DLFM），用于动态选择并整合来自不同模态的可靠信息。大量实验表明，MDAA在所提出的各项任务上均达到最先进性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
