AlignMamba-2: Enhancing Multimodal Fusion and Sentiment Analysis with Modality-Aware Mamba
arXiv:2603.18462v1 Announce Type: new Abstract: In the era of large-scale pre-trained models, effectively adapting general knowledge to specific affective computing tasks remains a challenge, particularly regarding computational efficiency and multimodal heterogeneity. While Transformer-based methods have excelled at modeling inter-modal dependencies, their quadratic computational complexity limits their use with long-sequence data. Mamba-based models have emerged as a computationally efficient alternative; however, their inherent sequential scanning mechanism struggles to capture the global, non-sequential relationships that are crucial for effective cross-modal alignment. To address these limitations, we propose \textbf{AlignMamba-2}, an effective and efficient framework for multimodal fusion and sentiment analysis. Our approach introduces a dual alignment strategy that regularizes the model using both Optimal Transport distance and Maximum Mean Discrepancy, promoting geometric and
arXiv:2603.18462v1 Announce Type: new Abstract: In the era of large-scale pre-trained models, effectively adapting general knowledge to specific affective computing tasks remains a challenge, particularly regarding computational efficiency and multimodal heterogeneity. While Transformer-based methods have excelled at modeling inter-modal dependencies, their quadratic computational complexity limits their use with long-sequence data. Mamba-based models have emerged as a computationally efficient alternative; however, their inherent sequential scanning mechanism struggles to capture the global, non-sequential relationships that are crucial for effective cross-modal alignment. To address these limitations, we propose \textbf{AlignMamba-2}, an effective and efficient framework for multimodal fusion and sentiment analysis. Our approach introduces a dual alignment strategy that regularizes the model using both Optimal Transport distance and Maximum Mean Discrepancy, promoting geometric and statistical consistency between modalities without incurring any inference-time overhead. More importantly, we design a Modality-Aware Mamba layer, which employs a Mixture-of-Experts architecture with modality-specific and modality-shared experts to explicitly handle data heterogeneity during the fusion process. Extensive experiments on four challenging benchmarks, including dynamic time-series (on the CMU-MOSI and CMU-MOSEI datasets) and static image-related tasks (on the NYU-Depth V2 and MVSA-Single datasets), demonstrate that AlignMamba-2 establishes a new state-of-the-art in both effectiveness and efficiency across diverse pattern recognition tasks, ranging from dynamic time-series analysis to static image-text classification.
Executive Summary
AlignMamba-2 is a novel framework proposed to address the limitations of existing multimodal fusion and sentiment analysis models. By introducing a dual alignment strategy and a Modality-Aware Mamba layer, AlignMamba-2 achieves computational efficiency and effective cross-modal alignment. The framework demonstrates state-of-the-art performance on four challenging benchmarks, showcasing its adaptability across diverse pattern recognition tasks. While the approach appears promising, its applicability and scalability in real-world scenarios remain to be explored. The authors' efforts to alleviate the computational complexity of Transformer-based methods and to explicitly handle data heterogeneity are noteworthy contributions to the field of affective computing.
Key Points
- ▸ AlignMamba-2 addresses the limitations of Mamba-based models in capturing global, non-sequential relationships in multimodal fusion and sentiment analysis.
- ▸ The proposed framework introduces a dual alignment strategy and a Modality-Aware Mamba layer to promote geometric and statistical consistency between modalities.
- ▸ AlignMamba-2 achieves state-of-the-art performance on four challenging benchmarks, demonstrating its effectiveness and efficiency in diverse pattern recognition tasks.
Merits
Strength
AlignMamba-2's dual alignment strategy and Modality-Aware Mamba layer effectively address the limitations of existing multimodal fusion and sentiment analysis models.
Demerits
Limitation
The framework's applicability and scalability in real-world scenarios, particularly in terms of computational resources and data complexity, are unclear and require further investigation.
Expert Commentary
The authors' contribution to the field of affective computing is significant, as they address the limitations of existing multimodal fusion and sentiment analysis models. The proposed framework's ability to adapt to diverse pattern recognition tasks is noteworthy, and its performance on challenging benchmarks is impressive. However, further investigation is needed to fully understand the framework's applicability and scalability in real-world scenarios. The authors' efforts to alleviate the computational complexity of Transformer-based methods and to explicitly handle data heterogeneity are valuable contributions to the field. Overall, AlignMamba-2 is a promising approach that warrants further exploration and development.
Recommendations
- ✓ Future research should focus on evaluating AlignMamba-2's performance in real-world scenarios, considering computational resources and data complexity.
- ✓ The authors should investigate the framework's adaptability to other affective computing tasks, such as emotion recognition and human-computer interaction.
