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Modality-Invariant Bidirectional Temporal Representation Distillation Network for Missing Multimodal Sentiment Analysis

Xincheng Wang, Liejun Wang, Yinfeng Yu, Xinxin Jiao

TL;DR

This work tackles missing data in multimodal sentiment analysis by introducing MITR-DNet, which unifies modality-invariant bidirectional temporal representation learning (MIB-TRL) with a Transformer Fusion strategy and a distillation framework from a complete-modality teacher to a missing-modality student. By jointly addressing reconstruction and representation learning, the approach mitigates inter-modal heterogeneity while preserving informative signals when modalities are absent. Empirical results on CMU-MOSI and CH-SIMS show strong performance gains in both complete and incomplete modality settings, with ablations validating the necessity of the TF module and the distillation-reconstruction-SimSiam loss trio. The proposed framework offers practical resilience for real-world MSA under random modality dropouts and heterogeneous data, advancing robustness in multimodal sentiment prediction.

Abstract

Multimodal Sentiment Analysis (MSA) integrates diverse modalities(text, audio, and video) to comprehensively analyze and understand individuals' emotional states. However, the real-world prevalence of incomplete data poses significant challenges to MSA, mainly due to the randomness of modality missing. Moreover, the heterogeneity issue in multimodal data has yet to be effectively addressed. To tackle these challenges, we introduce the Modality-Invariant Bidirectional Temporal Representation Distillation Network (MITR-DNet) for Missing Multimodal Sentiment Analysis. MITR-DNet employs a distillation approach, wherein a complete modality teacher model guides a missing modality student model, ensuring robustness in the presence of modality missing. Simultaneously, we developed the Modality-Invariant Bidirectional Temporal Representation Learning Module (MIB-TRL) to mitigate heterogeneity.

Modality-Invariant Bidirectional Temporal Representation Distillation Network for Missing Multimodal Sentiment Analysis

TL;DR

This work tackles missing data in multimodal sentiment analysis by introducing MITR-DNet, which unifies modality-invariant bidirectional temporal representation learning (MIB-TRL) with a Transformer Fusion strategy and a distillation framework from a complete-modality teacher to a missing-modality student. By jointly addressing reconstruction and representation learning, the approach mitigates inter-modal heterogeneity while preserving informative signals when modalities are absent. Empirical results on CMU-MOSI and CH-SIMS show strong performance gains in both complete and incomplete modality settings, with ablations validating the necessity of the TF module and the distillation-reconstruction-SimSiam loss trio. The proposed framework offers practical resilience for real-world MSA under random modality dropouts and heterogeneous data, advancing robustness in multimodal sentiment prediction.

Abstract

Multimodal Sentiment Analysis (MSA) integrates diverse modalities(text, audio, and video) to comprehensively analyze and understand individuals' emotional states. However, the real-world prevalence of incomplete data poses significant challenges to MSA, mainly due to the randomness of modality missing. Moreover, the heterogeneity issue in multimodal data has yet to be effectively addressed. To tackle these challenges, we introduce the Modality-Invariant Bidirectional Temporal Representation Distillation Network (MITR-DNet) for Missing Multimodal Sentiment Analysis. MITR-DNet employs a distillation approach, wherein a complete modality teacher model guides a missing modality student model, ensuring robustness in the presence of modality missing. Simultaneously, we developed the Modality-Invariant Bidirectional Temporal Representation Learning Module (MIB-TRL) to mitigate heterogeneity.
Paper Structure (12 sections, 20 equations, 4 figures, 4 tables)

This paper contains 12 sections, 20 equations, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Methodologies
  3. MIB-TRL module
  4. Transformer Fusion (TF)
  5. Training objective
  6. Experimental Setup
  7. Datasets
  8. Implementation details
  9. Experimental results
  10. Comparison with state-of-the-art technology
  11. Ablation experiment
  12. Conclusion

Figures (4)

  • Figure 1: Traditional framework vs. our proposed framework.
  • Figure 2: Present the overall framework of the MITR-DNet methodology.
  • Figure 3: Visualisation of model prediction labels on CH-SIMS dataset. Dark red indicates the most negative emotion (-1). Dark green indicates the most positive emotion (1). Dark grey indicates the most neutral emotion (0). The deeper the color, the stronger the emotion.
  • Figure 4: Training loss trends under different modality missing rates