MARLIN: Masked Autoencoder for facial video Representation LearnINg (2211.06627v3)

Abstract: This paper proposes a self-supervised approach to learn universal facial representations from videos, that can transfer across a variety of facial analysis tasks such as Facial Attribute Recognition (FAR), Facial Expression Recognition (FER), DeepFake Detection (DFD), and Lip Synchronization (LS). Our proposed framework, named MARLIN, is a facial video masked autoencoder, that learns highly robust and generic facial embeddings from abundantly available non-annotated web crawled facial videos. As a challenging auxiliary task, MARLIN reconstructs the spatio-temporal details of the face from the densely masked facial regions which mainly include eyes, nose, mouth, lips, and skin to capture local and global aspects that in turn help in encoding generic and transferable features. Through a variety of experiments on diverse downstream tasks, we demonstrate MARLIN to be an excellent facial video encoder as well as feature extractor, that performs consistently well across a variety of downstream tasks including FAR (1.13% gain over supervised benchmark), FER (2.64% gain over unsupervised benchmark), DFD (1.86% gain over unsupervised benchmark), LS (29.36% gain for Frechet Inception Distance), and even in low data regime. Our code and models are available at https://github.com/ControlNet/MARLIN .

• The paper introduces MARLIN, a masked autoencoder that uses facial-region guided masking to learn robust facial video embeddings from unlabeled data.
• The methodology integrates a Vision Transformer backbone and adversarial training, achieving significant gains including a 29.36% improvement in lip synchronization metrics.
• The framework demonstrates versatility in facial attribute recognition, expression detection, deepfake detection, and lip synchronization, performing well under few-shot conditions.

Overview of MARLIN: A Study on Self-Supervised Facial Video Representation Learning

The research paper titled "MARLIN: Masked Autoencoder for Facial Video Representation Learning" presents an innovative approach to learning universal facial representations from video data in a self-supervised manner. The primary focus of this paper is the development of a framework named MARLIN, which is a facial video masked autoencoder designed to learn robust and generic facial embeddings. This approach is particularly noteworthy as it leverages non-annotated facial video data, addressing a significant limitation in the field where large-scale annotated datasets are often required.

Methodology

MARLIN aims to provide transferable features across various facial analysis tasks including Facial Attribute Recognition (FAR), Facial Expression Recognition (FER), DeepFake Detection (DFD), and Lip Synchronization (LS). The core innovation lies in MARLIN's ability to reconstruct spatio-temporal details from densely masked facial regions—such as eyes, nose, and mouth—thereby capturing both local and global aspects of the face. This is achieved through a facial-region guided masking strategy, which challenges the encoder with the task of reconstructing the original facial video from partial observations, thus promoting the learning of comprehensive representations.

A key component of MARLIN is the integration of adversarial training, which enhances the reconstruction quality and facilitates the learning of rich and generic latent features. The architecture employs Vision Transformers (ViT) as the backbone, utilizing a high masking ratio to optimize learning efficiency.

Experimental Results

The experiments conducted using diverse downstream tasks demonstrate the efficacy of MARLIN as one of the leading facial video encoders. Significant performance improvements were recorded across various tasks, with a notable 29.36% improvement in Lip Synchronization for the Frechet Inception Distance metric, and other competitive gains in FAR, FER, and DFD tasks compared to both supervised and unsupervised benchmarks.

The research further explores the robustness and adaptability of MARLIN by demonstrating its performance under few-shot learning settings. The results indicate that MARLIN is capable of maintaining relatively high effectiveness even with severely limited annotated data, thus supporting its potential for practical application where data annotation is constrained.

Implications

From a practical standpoint, MARLIN holds promise for applications in Human-Computer Interaction (HCI), Affective Computing, and security domains such as DeepFake detection. The self-supervised nature of MARLIN removes the burden of large-scale annotated dataset requirements, thus making it suitable for real-world deployment, including edge computing scenarios.

Theoretically, this paper contributes to the discourse on self-supervised learning, particularly in demonstrating its applicability beyond traditional natural scene images to more complex domains such as facial video analysis. It highlights the adaptability of masked autoencoders and establishes a foundation for future research in exploring spatio-temporal representation learning without extensive labeled data.

Future Directions

Looking ahead, the paper suggests exploring MARLIN's potential bias due to the dataset used during training, which primarily contains faces from certain racial and cultural backgrounds. Future research could focus on expanding data diversity or employing debiasing techniques to enhance the model's generalizability across diverse populations.

Moreover, the integration of MARLIN with real-time processing capabilities could open avenues for its application in interactive systems and devices with constrained computational resources, thereby broadening the scope of its practical utility.