Contrastive Mixed Prompt Learning for Incomplete Multimodal Sentiment Analysis with Unseen Modality Combination

Discriminative semantic learning for incomplete multimodal sentiment analysis

The framework first introduces learnable sentiment prototypes as semantic anchors to provide explicit sentiment-discriminative guidance for feature completion, and a gradient decoupling strategy is designed to separate the optimization paths of unimodal and multimodal objectives, preventing fusion gradients from interfering with unimodal encoders, thereby synergistically enhancing both discriminative representation learning and multimodal fusion.

Dual-path fusion network for multimodal sentiment analysis under uncertain missing modalities

Multimodal Sentiment Analysis via Modal Experts and Missing-Modal Prompt Generation

Multimodal sentiment analysis aims to extract affective signals from multiple modalities and perform feature analysis, modality fusion and sentiment prediction. However, in practical application scenarios, input modality information is highly prone to be missing due to various objective factors, which further causes the model to produce biased or even completely erroneous sentiment judgment results. To address this critical issue, a model named Modal Experts and Missing Prompt Generation (MEMPG) is proposed. The model employs a brand-new dual-residual modality expert architecture to integrate the knowledge of hybrid modality experts. This architecture can not only retain the original information but also preserve the contextual information learned by the attention mechanism, thus exhibiting better robustness. The model adopts a twostage training strategy: In the first stage, each modality expert branch is independently pre-trained to extract low-level basic features of a single modality and strengthen the feature extraction capability of modality experts. In the second stage, all input modality features are fed into all modality expert branches for processing, and adaptive weights are assigned to modality features via an adaptive gating mechanism to obtain fused modality features with stronger representation ability. Subsequently, the enhanced features are input into the missing modality prompt generation module, which incorporates prompt learning to guide the reconstruction of missing modalities. Extensive comparative experiments are conducted on two standard multimodal sentiment datasets, namely MOSI and MOSEI, under various common modality missing scenarios. Experimental results demonstrate that, compared with current mainstream baseline models, the proposed MEMPG model achieves superior sentiment prediction performance.

Register-augmented attention and self-calibrated fusion for robust multimodal sentiment analysis

Multimodal sentiment analysis (MSA) aims to understand human emotions by integrating heterogeneous modalities such as language and acoustic signals. Despite recent progress, two key challenges remain: (1) inter-modal inconsistency, where different modalities may convey conflicting sentiment cues, and (2) intra-modal feature ambiguity caused by noise and subtle emotional variations. To address these issues, we propose RegCal-Net, a register-augmented and self-calibrated framework for bimodal sentiment analysis. First, we introduce a Register-Augmented Self-Attention (RASA) mechanism that appends learnable register tokens along the sequence dimension to provide auxiliary global anchors for each modality. Second, we design a Self-Calibrated Fusion (SCF) module that dynamically evaluates fused feature reliability through an auxiliary score-guided gating strategy, enabling adaptive suppression of unreliable signals during multimodal integration. Extensive experiments on two widely used benchmarks, CMU-MOSI and CMU-MOSEI, together with CMU-MOSI encoder-controlled baselines and a supplementary video-subset validation, demonstrate that RegCal-Net achieves competitive performance across multiple evaluation metrics, particularly improving fine-grained sentiment classification and reducing prediction error. These results indicate that combining register-based representation stabilization with quality-aware fusion provides an effective solution for robust multimodal sentiment analysis.

Learning Adaptive Cross-Modal Interactions for Multimodal Sentiment Analysis

A framework for learning adaptive cross-modal interactions for multimodal sentiment analysis that consistently outperforms previous methods and enhances multimodal representation capability for sentiment classification is proposed.

Consistency-Aware Gated Fusion with Mamba for Multimodal Sentiment Analysis

Consistency-Aware Gated Fusion (CAGF), a lightweight and fusion module tailored to Mamba-based architectures that achieves state-of-the-art performance, outperforming strong multimodal baselines such as CLIP, MISA, DLF, AoM, and SFTTR, while remaining more efficient and interpretable.