The Gate-Controlled Information Bottleneck Cross-Modal Attention Network (GICA), a two-stage hierarchical framework that jointly optimizes adaptive modal compression and cross-modal information interaction, is offered, addressing the issues of over-compression and under-compression present in current information bottleneck techniques.
HAFT routes audio–visual interaction through bottleneck tokens with adaptive depth-wise gating before integrating text, jointly reducing attention cost and counteracting modality bias; the Cascaded Audio Feature Enhancement (CAFE) framework strengthens prosodic representations via multi-scale time–frequency extraction; and grouped projections, decoupled positional attention, and layer-wise parameter sharing compress the remaining overhead.
Qing Dong, Ting Lu, Xiujin Shi et al.· International journal of sof...· 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.
TC-DSC is proposed, a text-centric hierarchical dual-stream interaction framework for incomplete multimodal sentiment analysis that achieves competitive performance and consistent improvements under both complete and incomplete settings.
Jinjin Liu, Changchang Fan, Qiulu Guo et al.· Computers, Materials & C...· 0 citations
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.
Xu Meng, Yi Zhang, Yang Li· 2026 8th International Confe...· 0 citations
The proposed architecture provides an effective and interpretable framework for robust multimodal sentiment analysis and offers a promising foundation for real-world affective computing applications.
B. Ankayarkanni, D. Nandini, P. Sangeetha et al.· International journal of com...· 0 citations
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.
Bin Song, Wenjing Liu, Zhi Liang et al.· Signal, Image and Video Proc...· 0 citations
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