Audio-Guided Fusion Techniques for Multimodal Emotion Analysis
<|reference_start|>Audio-Guided Fusion Techniques for Multimodal Emotion Analysis: In this paper, we propose a solution for the semi-supervised learning track (MER-SEMI) in MER2024. First, in order to enhance the performance of the feature extractor on sentiment classification tasks,we fine-tuned video and text feature extractors, specifically CLIP-vit-large and Baichuan-13B, using labeled data. This approach effectively preserves the original emotional information conveyed in the videos. Second, we propose an Audio-Guided Transformer (AGT) fusion mechanism, which leverages the robustness of Hubert-large, showing superior effectiveness in fusing both inter-channel and intra-channel information. Third, To enhance the accuracy of the model, we iteratively apply self-supervised learning by using high-confidence unlabeled data as pseudo-labels. Finally, through black-box probing, we discovered an imbalanced data distribution between the training and test sets. Therefore, We adopt a prior-knowledge-based voting mechanism. The results demonstrate the effectiveness of our strategy, ultimately earning us third place in the MER-SEMI track.<|reference_end|>
@article{shi2024audio-guided,
    title={Audio-Guided Fusion Techniques for Multimodal Emotion Analysis},
    author={Pujin Shi and Fei Gao},
    journal={arXiv preprint arXiv:2409.05007},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.05007},
    primaryClass={cs.SD cs.AI eess.AS}
}