An Adaptive Multimodal Fusion with Hierarchical Semantic Disentanglement with Hierarchical Semantic Disentanglement is proposed, which achieves robust multimodal representation learning through uncertainty-aware dynamic calibration, alongside semantic disentanglement and refinement.
A novel reliability-aware disentangled adaptive network that consists of three components, dynamically modulating per-modality contributions by information quality to mitigate misleading effects of unreliable modalities is proposed.
Jia-Hao Xu, Xue-Feng Zhao, Li Jia et al.· International Journal of Dat...· 0 citations
DiMoE is proposed, a representation-first framework that integrates feature disentanglement with sparse Mixture of Experts (MoE) fusion, indicating that explicit modality disentanglement combined with Mixture of Experts fusion yields effective multimodal affective representations.
M. S. Sran, S. Ramanna, K. Kotecha· Algorithms· 0 citations
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
CHSIM, a unified MSA framework for robust text–image sentiment modeling that combines three complementary mechanisms: prediction-consistency regularization under stochastic latent masking, hierarchical cross-modal alignment, and global bidirectional sequence modeling, is proposed.
Siyang Zhang, Guang-Li Zhu, Qianjin Zhao et al.· Journal of Supercomputing· 0 citations
A Variational Autoencoder-Based Multi-Granularity Missing-Aware Dual-Path Fusion Network (VMMD) with contrastive learning-driven Cross-Path Semantic Consistency Constraint (CSCC), which mitigates semantic representation inconsistency caused by granularity heterogeneity between supplementary information and proxy featur...
Jia-Xu Li, Wei Liu· International Journal of Mac...· 0 citations
Results indicate that HiPDA provides a structured and effective framework for multimodal sentiment modeling, which organizes multimodal modeling in a progressive injection–fusion–alignment manner.
Zhi-Wen Liao, Ke Qi, Jia-Xiong Liu et al.· Multimedia Systems· 0 citations
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