Analytic Continual Test-Time Adaptation for Multi-Modality Corruption

• URL: http://arxiv.org/abs/2410.22373v1
• Date: Tue, 29 Oct 2024 01:21:24 GMT
• Title: Analytic Continual Test-Time Adaptation for Multi-Modality Corruption
• Authors: Yufei Zhang, Yicheng Xu, Hongxin Wei, Zhiping Lin, Huiping Zhuang,
• Abstract summary: Test-Time Adaptation (TTA) aims to help pre-trained models bridge the gap between source and target datasets. We propose a novel approach, Multi-modality Dynamic Analytic Adapter (MDAA) for MM-CTTA tasks. MDAA achieves state-of-the-art performance on MM-CTTA while ensuring reliable model adaptation.
• Score: 23.545997349882857
• License:
• Abstract: Test-Time Adaptation (TTA) aims to help pre-trained model bridge the gap between source and target datasets using only the pre-trained model and unlabelled test data. A key objective of TTA is to address domain shifts in test data caused by corruption, such as weather changes, noise, or sensor malfunctions. Multi-Modal Continual Test-Time Adaptation (MM-CTTA), an extension of TTA with better real-world applications, further allows pre-trained models to handle multi-modal inputs and adapt to continuously-changing target domains. MM-CTTA typically faces challenges including error accumulation, catastrophic forgetting, and reliability bias, with few existing approaches effectively addressing these issues in multi-modal corruption scenarios. In this paper, we propose a novel approach, Multi-modality Dynamic Analytic Adapter (MDAA), for MM-CTTA tasks. We innovatively introduce analytic learning into TTA, using the Analytic Classifiers (ACs) to prevent model forgetting. Additionally, we develop Dynamic Selection Mechanism (DSM) and Soft Pseudo-label Strategy (SPS), which enable MDAA to dynamically filter reliable samples and integrate information from different modalities. Extensive experiments demonstrate that MDAA achieves state-of-the-art performance on MM-CTTA tasks while ensuring reliable model adaptation.

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