Efficient Feature Extraction and Late Fusion Strategy for Audiovisual Emotional Mimicry Intensity Estimation
<|reference_start|>Efficient Feature Extraction and Late Fusion Strategy for Audiovisual Emotional Mimicry Intensity Estimation: In this paper, we present the solution to the Emotional Mimicry Intensity (EMI) Estimation challenge, which is part of 6th Affective Behavior Analysis in-the-wild (ABAW) Competition.The EMI Estimation challenge task aims to evaluate the emotional intensity of seed videos by assessing them from a set of predefined emotion categories (i.e., "Admiration", "Amusement", "Determination", "Empathic Pain", "Excitement" and "Joy"). To tackle this challenge, we extracted rich dual-channel visual features based on ResNet18 and AUs for the video modality and effective single-channel features based on Wav2Vec2.0 for the audio modality. This allowed us to obtain comprehensive emotional features for the audiovisual modality. Additionally, leveraging a late fusion strategy, we averaged the predictions of the visual and acoustic models, resulting in a more accurate estimation of audiovisual emotional mimicry intensity. Experimental results validate the effectiveness of our approach, with the average Pearson's correlation Coefficient($\rho$) across the 6 emotion dimensionson the validation set achieving 0.3288.<|reference_end|>
@article{yu2024efficient,
    title={Efficient Feature Extraction and Late Fusion Strategy for Audiovisual
  Emotional Mimicry Intensity Estimation},
    author={Jun Yu, Wangyuan Zhu, Jichao Zhu},
    journal={arXiv preprint arXiv:2403.11757},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11757},
    primaryClass={cs.MM cs.LG cs.SD eess.AS}
}