Papers › Semantic-Guided Multimodal Sentiment Decoding with Adversarial Temporal-Invariant Learning

Semantic-Guided Multimodal Sentiment Decoding with Adversarial Temporal-Invariant Learning

30 Aug 2024arXiv:2409.00143archive 2025-07-28

Guoyang Xu, Junqi Xue, Yuxin Liu, ZiRui Wang, Min Zhang, Zhenxi Song, Zhiguo Zhang

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.

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