Semantic-Guided Multimodal Sentiment Decoding with Adversarial Temporal-Invariant Learning
<|reference_start|>Semantic-Guided Multimodal Sentiment Decoding with Adversarial Temporal-Invariant Learning: Multimodal sentiment analysis aims to learn representations from different modalities to identify human emotions. However, existing works often neglect the frame-level redundancy inherent in continuous time series, resulting in incomplete modality representations with noise. To address this issue, we propose temporal-invariant learning for the first time, which constrains the distributional variations over time steps to effectively capture long-term temporal dynamics, thus enhancing the quality of the representations and the robustness of the model. To fully exploit the rich semantic information in textual knowledge, we propose a semantic-guided fusion module. By evaluating the correlations between different modalities, this module facilitates cross-modal interactions gated by modality-invariant representations. Furthermore, we introduce a modality discriminator to disentangle modality-invariant and modality-specific subspaces. Experimental results on two public datasets demonstrate the superiority of our model. Our code is available at https://github.com/X-G-Y/SATI.<|reference_end|>
@article{xu2024semantic-guided,
    title={Semantic-Guided Multimodal Sentiment Decoding with Adversarial
  Temporal-Invariant Learning},
    author={Guoyang Xu, Junqi Xue, Yuxin Liu, Zirui Wang, Min Zhang, Zhenxi Song
  and Zhiguo Zhang},
    journal={arXiv preprint arXiv:2409.00143},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00143},
    primaryClass={cs.LG cs.AI cs.CV}
}