Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation

TL;DR

Continual-MAE introduces an adaptive self-supervised autoencoder approach for continual test-time adaptation, effectively handling distribution shifts and improving performance in classification and segmentation tasks.

Contribution

It proposes a novel Distribution-aware Masking mechanism and a reconstruction strategy using invariant feature descriptors to enhance knowledge extraction during continual adaptation.

Findings

Achieves state-of-the-art results on multiple benchmarks.

Effectively mitigates error accumulation and catastrophic forgetting.

Improves robustness to distribution shifts in real-world scenarios.

Abstract

Continual Test-Time Adaptation (CTTA) is proposed to migrate a source pre-trained model to continually changing target distributions, addressing real-world dynamism. Existing CTTA methods mainly rely on entropy minimization or teacher-student pseudo-labeling schemes for knowledge extraction in unlabeled target domains. However, dynamic data distributions cause miscalibrated predictions and noisy pseudo-labels in existing self-supervised learning methods, hindering the effective mitigation of error accumulation and catastrophic forgetting problems during the continual adaptation process. To tackle these issues, we propose a continual self-supervised method, Adaptive Distribution Masked Autoencoders (ADMA), which enhances the extraction of target domain knowledge while mitigating the accumulation of distribution shifts. Specifically, we propose a Distribution-aware Masking (DaM) mechanism to adaptively sample masked positions, followed by establishing consistency constraints between the masked target samples and the original target samples. Additionally, for masked tokens, we utilize an efficient decoder to reconstruct a hand-crafted feature descriptor (e.g., Histograms of Oriented Gradients), leveraging its invariant properties to boost task-relevant representations. Through conducting extensive experiments on four widely recognized benchmarks, our proposed method attains state-of-the-art performance in both classification and segmentation CTTA tasks. Our project page: https://sites.google.com/view/continual-mae/home.