SVFAP: Self-supervised Video Facial Affect Perceiver

TL;DR

SVFAP introduces a self-supervised learning framework for video facial affect analysis that leverages masked autoencoding and a novel Transformer architecture, significantly improving performance across multiple affect recognition tasks without requiring labeled data.

Contribution

The paper proposes SVFAP, a self-supervised approach with a novel Transformer encoder, enabling effective large-scale pre-training on unlabeled videos for facial affect analysis.

Findings

Outperforms state-of-the-art on nine datasets

Effective in multiple affect recognition tasks

Reduces computational costs with novel Transformer design

Abstract

Video-based facial affect analysis has recently attracted increasing attention owing to its critical role in human-computer interaction. Previous studies mainly focus on developing various deep learning architectures and training them in a fully supervised manner. Although significant progress has been achieved by these supervised methods, the longstanding lack of large-scale high-quality labeled data severely hinders their further improvements. Motivated by the recent success of self-supervised learning in computer vision, this paper introduces a self-supervised approach, termed Self-supervised Video Facial Affect Perceiver (SVFAP), to address the dilemma faced by supervised methods. Specifically, SVFAP leverages masked facial video autoencoding to perform self-supervised pre-training on massive unlabeled facial videos. Considering that large spatiotemporal redundancy exists in facial videos, we propose a novel temporal pyramid and spatial bottleneck Transformer as the encoder of SVFAP, which not only largely reduces computational costs but also achieves excellent performance. To verify the effectiveness of our method, we conduct experiments on nine datasets spanning three downstream tasks, including dynamic facial expression recognition, dimensional emotion recognition, and personality recognition. Comprehensive results demonstrate that SVFAP can learn powerful affect-related representations via large-scale self-supervised pre-training and it significantly outperforms previous state-of-the-art methods on all datasets. Code is available at https://github.com/sunlicai/SVFAP.