Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression Recognition
<|reference_start|>Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression Recognition: Human communication is multi-modal; e.g., face-to-face interaction involves auditory signals (speech) and visual signals (face movements and hand gestures). Hence, it is essential to exploit multiple modalities when designing machine learning-based facial expression recognition systems. In addition, given the ever-growing quantities of video data that capture human facial expressions, such systems should utilize raw unlabeled videos without requiring expensive annotations. Therefore, in this work, we employ a multitask multi-modal self-supervised learning method for facial expression recognition from in-the-wild video data. Our model combines three self-supervised objective functions: First, a multi-modal contrastive loss, that pulls diverse data modalities of the same video together in the representation space. Second, a multi-modal clustering loss that preserves the semantic structure of input data in the representation space. Finally, a multi-modal data reconstruction loss. We conduct a comprehensive study on this multimodal multi-task self-supervised learning method on three facial expression recognition benchmarks. To that end, we examine the performance of learning through different combinations of self-supervised tasks on the facial expression recognition downstream task. Our model ConCluGen outperforms several multi-modal self-supervised and fully supervised baselines on the CMU-MOSEI dataset. Our results generally show that multi-modal self-supervision tasks offer large performance gains for challenging tasks such as facial expression recognition, while also reducing the amount of manual annotations required. We release our pre-trained models as well as source code publicly<|reference_end|>
@article{halawa2024multi-task,
    title={Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression
  Recognition},
    author={Marah Halawa and Florian Blume and Pia Bideau and Martin Maier and
  Rasha Abdel Rahman and Olaf Hellwich},
    journal={Proceedings of the IEEE/CVF Conference on Computer Vision and
  Pattern Recognition (CVPR) Workshops, 2024, pp. 4604-4614},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.10904},
    primaryClass={cs.CV}
}