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.
Abstract
With the rise of social media, multimodal sentiment analysis has gained increasing attention due to the rich and diverse ways users express emotions through text and images. However, existing methods often ignore fine-grained sentiment cues and semantic misalignment across modalities, limiting this task effectiveness. To address these challenges, we propose DualScope, a novel model that combines a global-local fusion strategy with bidirectional image-text generation for semantically consistent data augmentation. Furthermore, we introduce both label contrastive learning and data contrastive learning to align heterogeneous modalities and enhance model robustness. The fusion module integrates global and local features in a progressive manner under global semantic guidance. Extensive experiments on two widely used datasets, MVSA-Single and MVSA-Multiple, demonstrate that the proposed method achieves superior performance, confirming its effectiveness in handling fine-grained semantics and cross-modal inconsistency.
A Modality Dropout strategy is first introduced at the input stage to alleviate over-reliance on a sin-gle modality and improve robustness and the proposed Hierarchical Global-Local Interaction and Refinement framework for Multimodal Sentiment Analysis (HGLIR) is proposed.
yuanyuan zhou· Poster Volume 0007 The 2026...· 0 citations
A Global Tri-Modality Transformer (GTMT) that first performs parallel fusion of the three modalities and then conducts deep integration guided by the textual modality to achieve the cross-modal semantic alignment and correlation, significantly improving the global unified fusion effectiveness of the tri-modality information.
Zelong Li, Shuhua Lu, Fang Cui et al.· Neural Networks· 0 citations
Less obvious expressions like sarcasm and implicit sentiment are handled more effectively in this work, improving interpretation in multimodal sentiment analysis of social media data.
Prashant Adakane, Amit Gaikwad· international journal of eng...· 0 citations
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.
Shanshan Lin, Yuesheng Wu, Chao Chen et al.· 0 citations
Multimodal sentiment analysis has attracted increasing attention due to the prevalence of text-image content on social media. A central challenge is to design fusion mechanisms that are both expressive and parameter-efficient, especially for small-scale datasets where heavy cross-modal attention can easily overfit. In this paper, we present Consistency-Aware Gated Fusion (CAGF), a lightweight and fusion module tailored to Mamba-based architectures. Our key idea is to exploit Mamba's bidirectional scanning mechanism: forward and backward hidden states from text and image encoders are concatenated to form enhanced representations, and a cosine-based semantic consistency score is computed between modalities. This score is then passed through a fixed sigmoid gate to adaptively weight text and image features, without introducing any additional learnable parameters. CAGF is plug-and-play compatible with dual-stream Mamba encoders and incurs negligible computational overhead compared with attention-based fusion. Experiments on the MVSA-Single dataset show that CAGF achieves state-of-the-art performance (Acc=82.54%, F1=84.82%), outperforming strong multimodal baselines such as CLIP, MISA, DLF, AoM, and SFTTR, while remaining more efficient and interpretable. Extensive ablations and sensitivity analyses further validate that bidirectional scanning, enhanced representations, and consistency-aware gating are all critical to the observed gains.
Jian Hu· Poster Volume 0008 The 2026...· 0 citations
MagXCL, a unified framework designed to improve multimodal integration through more effective interaction between verbal and non-verbal modalities, is proposed, demonstrating the effectiveness of combining AMag with CrossCL to produce more accurate and robust multimodal sentiment predictions.