Leveraging Retrieval Augment Approach for Multimodal Emotion Recognition Under Missing Modalities
<|reference_start|>Leveraging Retrieval Augment Approach for Multimodal Emotion Recognition Under Missing Modalities: Multimodal emotion recognition utilizes complete multimodal information and robust multimodal joint representation to gain high performance. However, the ideal condition of full modality integrity is often not applicable in reality and there always appears the situation that some modalities are missing. For example, video, audio, or text data is missing due to sensor failure or network bandwidth problems, which presents a great challenge to MER research. Traditional methods extract useful information from the complete modalities and reconstruct the missing modalities to learn robust multimodal joint representation. These methods have laid a solid foundation for research in this field, and to a certain extent, alleviated the difficulty of multimodal emotion recognition under missing modalities. However, relying solely on internal reconstruction and multimodal joint learning has its limitations, especially when the missing information is critical for emotion recognition. To address this challenge, we propose a novel framework of Retrieval Augment for Missing Modality Multimodal Emotion Recognition (RAMER), which introduces similar multimodal emotion data to enhance the performance of emotion recognition under missing modalities. By leveraging databases, that contain related multimodal emotion data, we can retrieve similar multimodal emotion information to fill in the gaps left by missing modalities. Various experimental results demonstrate that our framework is superior to existing state-of-the-art approaches in missing modality MER tasks. Our whole project is publicly available on https://github.com/WooyoohL/Retrieval_Augment_MER.<|reference_end|>
@article{fan2024leveraging,
    title={Leveraging Retrieval Augment Approach for Multimodal Emotion Recognition
  Under Missing Modalities},
    author={Qi Fan, Hongyu Yuan, Haolin Zuo, Rui Liu and Guanglai Gao},
    journal={arXiv preprint arXiv:2410.02804},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02804},
    primaryClass={cs.CV cs.AI}
}