TY - JOUR

T1 - Frequency Decoupled Masked Auto-Encoder for Self-Supervised Skeleton-Based Action Recognition

AU - Liu, Ye

AU - Shi, Tianhao

AU - Zhai, Mingliang

AU - Liu, Jun

PY - 2025/1/3

Y1 - 2025/1/3

N2 - In 3D skeleton-based action recognition, the limited availability of supervised data has driven interest in self-supervised learning methods. The reconstruction paradigm using masked auto-encoder (MAE) is an effective and mainstream self-supervised learning approach. However, recent studies indicate that MAE models tend to focus on features within a certain frequency range, which may result in the loss of important information. To address this issue, we propose a frequency decoupled MAE. Specifically, by incorporating a scale-specific frequency feature reconstruction module, we delve into leveraging frequency information as a direct and explicit target for reconstruction, which augments the MAE's capability to discern and accurately reproduce diverse frequency attributes within the data. Moreover, in order to address the issue of unstable gradient updates caused by more complex optimization objectives with frequency reconstruction, we introduce a dual-path network combined with an exponential moving average (EMA) parameter updating strategy to guide the model in stabilizing the training process. We have conducted extensive experiments which have demonstrated the effectiveness of the proposed method.

AB - In 3D skeleton-based action recognition, the limited availability of supervised data has driven interest in self-supervised learning methods. The reconstruction paradigm using masked auto-encoder (MAE) is an effective and mainstream self-supervised learning approach. However, recent studies indicate that MAE models tend to focus on features within a certain frequency range, which may result in the loss of important information. To address this issue, we propose a frequency decoupled MAE. Specifically, by incorporating a scale-specific frequency feature reconstruction module, we delve into leveraging frequency information as a direct and explicit target for reconstruction, which augments the MAE's capability to discern and accurately reproduce diverse frequency attributes within the data. Moreover, in order to address the issue of unstable gradient updates caused by more complex optimization objectives with frequency reconstruction, we introduce a dual-path network combined with an exponential moving average (EMA) parameter updating strategy to guide the model in stabilizing the training process. We have conducted extensive experiments which have demonstrated the effectiveness of the proposed method.

U2 - 10.1109/lsp.2024.3525398

DO - 10.1109/lsp.2024.3525398

M3 - Journal article

SP - 546

EP - 550

JO - IEEE Signal Processing Letters

JF - IEEE Signal Processing Letters

SN - 1070-9908

ER -