AHSD: adaptive multimodal fusion with hierarchical semantic disentanglement for sentiment analysis

Reliability-aware disentangled adaptive network for multimodal sentiment analysis

DiMoE: Disentangled Representation Learning with Mixture of Experts Fusion for Sentiment Intensity Prediction and Emotion Classification

Multimodal affective analysis benefits from combining textual, acoustic, and visual cues, yet many methods implicitly mix modality-invariant information with modality-specific factors, which can reduce robustness when modalities vary in reliability. We propose DiMoE, a representation-first framework that integrates fea...

Hierarchical Global-Local Interaction and Refinement for Multimodal Sentiment Analysis

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.

CHSIM: counterfactual consistency and hierarchical modeling for semantic inconsistency in multimodal sentiment analysis

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.

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

HiPDA: hierarchical perceiver-style injection and dual-anchor alignment for multimodal sentiment analysis