Multimodal Channel-Mixing: Channel and Spatial Masked AutoEncoder on Facial Action Unit Detection

TL;DR

This paper introduces Multimodal Channel-Mixing, a novel early fusion masked autoencoder for facial Action Unit detection that improves multi-modal feature learning and surpasses existing methods.

Contribution

The paper proposes a new multi-modal reconstruction network with channel-mixing and masked autoencoding for robust AU detection, emphasizing early fusion and multi-modal learning.

Findings

Outperforms state-of-the-art baseline methods

Effective in learning robust multi-modal representations

Reduces channel redundancy and enhances fusion capabilities

Abstract

Recent studies have focused on utilizing multi-modal data to develop robust models for facial Action Unit (AU) detection. However, the heterogeneity of multi-modal data poses challenges in learning effective representations. One such challenge is extracting relevant features from multiple modalities using a single feature extractor. Moreover, previous studies have not fully explored the potential of multi-modal fusion strategies. In contrast to the extensive work on late fusion, there are limited investigations on early fusion for channel information exploration. This paper presents a novel multi-modal reconstruction network, named Multimodal Channel-Mixing (MCM), as a pre-trained model to learn robust representation for facilitating multi-modal fusion. The approach follows an early fusion setup, integrating a Channel-Mixing module, where two out of five channels are randomly dropped. The dropped channels then are reconstructed from the remaining channels using masked autoencoder. This module not only reduces channel redundancy, but also facilitates multi-modal learning and reconstruction capabilities, resulting in robust feature learning. The encoder is fine-tuned on a downstream task of automatic facial action unit detection. Pre-training experiments were conducted on BP4D+, followed by fine-tuning on BP4D and DISFA to assess the effectiveness and robustness of the proposed framework. The results demonstrate that our method meets and surpasses the performance of state-of-the-art baseline methods.