TF -Mamba: Text-enhanced Fusion Mamba with Missing Modalities for Robust Multimodal Sentiment Analysis (2025)

Abstract: Multimodal Sentiment Analysis (MSA) with missing modalities has attracted increasing attention recently. While current Transformer-based methods leverage dense text information to maintain model robustness, their quadratic complexity hinders efficient long-range modeling and multimodal fusion. To this end, we propose a novel and efficient Text-enhanced Fusion Mamba (TF-Mamba) framework for robust MSA with missing modalities. Specifically, a Text-aware Modality Enhancement (TME) module aligns and enriches non-text modalities, while reconstructing the missing text semantics. Moreover, we develop Text-based Context Mamba (TC-Mamba) to capture intra-modal contextual dependencies under text collaboration. Finally, Text-guided Query Mamba (TQ-Mamba) queries text-guided multimodal information and learns joint representations for sentiment prediction. Extensive experiments on three MSA datasets demonstrate the effectiveness and efficiency of the proposed method under missing modality scenarios. Code is available at https://github.com/codemous/TF-Mamba.

## introduction multimodal sentiment analysis (msa) aims to understand and integrate sentiment cues expressed in multiple modalities @xcite . previous studies @xcite @xcite demonstrate that integrating complementary multimodal information gains msa performance and enables its vital role in several applications. however, most existing methods @xcite @xcite @xcite are developed under ideal laboratory conditions where all modalities are assumed to be fully available during both training and inference. in real-world scenarios, many inevitable factors like background noise and sensor failures often result in incomplete or corrupted modalities. these challenges severely undermine the performance and robustness of msa models in practice. recent studies @xcite @xcite attempt to tackle the challenge of missing modalities in msa. among them, transformer-based fusion models achieve notable progress owing to their powerful sequence modeling capabilities. for example, tfr-net @xcite adopts a transformer-based feature reconstruction strategy to recover missing information in multimodal sequences. more recently, lnln @xcite prioritizes high-quality text features and treats text as the dominant modality to improve model robustness under various noise conditions. unfortunately, these approaches often suffer from quadratic computational complexity and fail to achieve efficient text-enhanced multimodal fusion. mamba @xcite ) emerges as a linear-time alternative to transformers, exhibiting great promise in various domains @xcite . nevertheless, incorporating text enhancement into mamba-based architectures for efficient missing modality modeling and fusion remains unexplored, which could potentially contribute to enhancing msa performance. to address these issues, we propose a novel and efficient text-enhanced fusion mamba (tf-mamba) framework for robust msa with missing modalities. tf-mamba features three core text-dominant components: text-aware modality enhancement (tme), text-based context mamba (tc-mamba), and text-guided query mamba (tq-mamba). specifically, the tme module first aligns and enhances audio and visual modalities with text features, and reconstructs missing textual semantics from incomplete inputs. subsequently, tc-mamba leverages text information as the collaborative bridge within mamba to model contextual dependencies in audio and video streams. the collaborative signals refine text representations in return for capturing shared semantics. finally, tq-mamba queries informative multimodal features via text-guided cross-attention. it then learns crossmodal interactions with mamba blocks to generate joint representations for sentiment prediction. the contributions are summarized as follows: