TL;DR
A novel method called Prompt Alignment and Multi-Granularity Feature Fusion (PAMFF), which employs prompt templates to construct prompts for aspect terms and then generates soft entity pseudo-labels derived from both the prompt features and the image entity features to achieve deeper cross-modal information fusion.
MGSI first encodes audio and visual streams at short-, medium-, and long-range temporal scales, preserving both local variations and global affective trends, and applies polarity- and intensity-aware enhancement to better handle ambiguous and near-neutral samples.
An Aspect-guided dual-branch fusion network (ADFN) to enhance sentiment prediction by incorporating external knowledge and integrating coarse and fine information is proposed, which incorporates syntactic dependency information to complement and enrich the textual semantic representations.
A modality-specific Graph Transformer with Prompt-aware Fusion (GTPF) framework that outperforms state-of-the-art methods across all metrics and validate the effectiveness of the Graph-Transformer co-design and prompt-aware fusion strategy.
DualScope is proposed, a novel model that combines a global-local fusion strategy with bidirectional image-text generation for semantically consistent data augmentation and introduces both label contrastive learning and data contrastive learning to align heterogeneous modalities and enhance model robustness.
In multimodal sentiment analysis, textual, acoustic, and visual modalities often contain redundant and noisy information. Such information increases model complexity and weakens core sentiment representations, degrading accuracy and robustness. To address this issue, we propose CLIBN, a multimodal sentiment recognition network based on contrastive learning and information bottleneck. First, we design a sentimentintensity-aware contrastive learning strategy. It constructs positive and negative pairs according to sentiment intensity distances and assigns adaptive weights to different pairs, enabling the model to capture fine-grained sentiment differences. Second, we introduce a hierarchical information bottleneck module. It treats text as the primary modality and progressively integrates complementary cues from acoustic and visual modalities, while preserving task-relevant semantics and suppressing redundant information. Experimental results on CMU-MOSI and CMU-MOSEI show that CLIBN achieves superior performance. Specifically, Acc-2 reaches 87.8% and 86.7%, and F1-Score reaches 87.8% and 86.6% on the two datasets, respectively. These results demonstrate the effectiveness of CLIBN for multimodal sentiment representation learning.
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.
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