Academic

CAGMamba: Context-Aware Gated Cross-Modal Mamba Network for Multimodal Sentiment Analysis

arXiv:2604.03650v1 Announce Type: new Abstract: Multimodal Sentiment Analysis (MSA) requires effective modeling of cross-modal interactions and contextual dependencies while remaining computationally efficient. Existing fusion approaches predominantly rely on Transformer-based cross-modal attention, which incurs quadratic complexity with respect to sequence length and limits scalability. Moreover, contextual information from preceding utterances is often incorporated through concatenation or independent fusion, without explicit temporal modeling that captures sentiment evolution across dialogue turns. To address these limitations, we propose CAGMamba, a context-aware gated cross-modal Mamba framework for dialogue-based sentiment analysis. Specifically, we organize the contextual and the current-utterance features into a temporally ordered binary sequence, which provides Mamba with explicit temporal structure for modeling sentiment evolution. To further enable controllable cross-modal

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Minghai Jiao, Jing Xiao, Peng Xiao, Ende Zhang, Shuang Kan, Wenyan Jiang, Jinyao Li, Yixian Liu, Haidong Xin · · 1 min read · 5 views
#cs.CL

arXiv:2604.03650v1 Announce Type: new Abstract: Multimodal Sentiment Analysis (MSA) requires effective modeling of cross-modal interactions and contextual dependencies while remaining computationally efficient. Existing fusion approaches predominantly rely on Transformer-based cross-modal attention, which incurs quadratic complexity with respect to sequence length and limits scalability. Moreover, contextual information from preceding utterances is often incorporated through concatenation or independent fusion, without explicit temporal modeling that captures sentiment evolution across dialogue turns. To address these limitations, we propose CAGMamba, a context-aware gated cross-modal Mamba framework for dialogue-based sentiment analysis. Specifically, we organize the contextual and the current-utterance features into a temporally ordered binary sequence, which provides Mamba with explicit temporal structure for modeling sentiment evolution. To further enable controllable cross-modal integration, we propose a Gated Cross-Modal Mamba Network (GCMN) that integrates cross-modal and unimodal paths via learnable gating to balance information fusion and modality preservation, and is trained with a three-branch multi-task objective over text, audio, and fused predictions. Experiments on three benchmark datasets demonstrate that CAGMamba achieves state-of-the-art or competitive results across multiple evaluation metrics. All codes are available at https://github.com/User2024-xj/CAGMamba.

Executive Summary

This article proposes a novel framework, CAGMamba, for Multimodal Sentiment Analysis (MSA) that effectively models cross-modal interactions and contextual dependencies. CAGMamba organizes contextual and current-utterance features into a temporally ordered binary sequence, enabling explicit temporal structure for sentiment evolution modeling. A Gated Cross-Modal Mamba Network (GCMN) integrates cross-modal and unimodal paths via learnable gating, balancing information fusion and modality preservation. Experiments on three benchmark datasets demonstrate state-of-the-art or competitive results across multiple evaluation metrics. While CAGMamba shows promise, its scalability and computational efficiency remain concerns.

Key Points

  • ▸ CAGMamba models cross-modal interactions and contextual dependencies for MSA
  • ▸ GCMN integrates cross-modal and unimodal paths via learnable gating
  • ▸ Explicit temporal structure enables sentiment evolution modeling

Merits

Strength in Modeling Complexity

CAGMamba effectively handles cross-modal interactions and contextual dependencies, addressing limitations of existing fusion approaches.

Demerits

Scalability Concerns

CAGMamba's computational efficiency remains uncertain due to the quadratic complexity of Transformer-based cross-modal attention.

Expert Commentary

While CAGMamba shows significant promise in addressing the complexities of MSA, its scalability and computational efficiency remain concerns. Future research should focus on optimizing the GCMN architecture and exploring more efficient multimodal fusion techniques. Additionally, the impact of CAGMamba on real-world applications and policy development should be further investigated.

Recommendations

  • ✓ Future work should aim to optimize the GCMN architecture for improved scalability and computational efficiency.
  • ✓ Investigate the application of CAGMamba in real-world dialogue systems and its potential policy implications.

Sources

Original: arXiv - cs.CL

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