Aug 2026· Multimedia Systems· Vol 32· 0 citations· 40 references
TL;DR
The framework first introduces learnable sentiment prototypes as semantic anchors to provide explicit sentiment-discriminative guidance for feature completion, and a gradient decoupling strategy is designed to separate the optimization paths of unimodal and multimodal objectives, preventing fusion gradients from interfering with unimodal encoders, thereby synergistically enhancing both discriminative representation learning and multimodal fusion.
A framework for learning adaptive cross-modal interactions for multimodal sentiment analysis that consistently outperforms previous methods and enhances multimodal representation capability for sentiment classification is proposed.
Chuhan Cheng, Hangcheng Wu, Jun-Qiao Wang et al.· International Conference on...· 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...
Xu Meng, Yi Zhang, Yang Li· 2026 8th International Confe...· 0 citations
The Adaptive Reliability-Guided Multimodal Artificial Intelligence system outperforms existing state-of-the-art multimodal learning algorithms under noisy and missing modality situations and provides consistent convergence and enhanced generalisation for real-world affective computing applications.
S. V· Journal of Intelligent Decis...· 0 citations
This paper introduces the problem of Incomplete Multimodal Sentiment Analysis with Unseen Modality Combinations (IMSAUMC), aiming to enhance model generalization for unseen modality combinations, and introduces a label-guided contrastive feature learning mechanism to learn robust and discriminative cross-modal represen...
Kaixin Xu, Nai-Jing Liu, Yu-Ri Kang et al.· 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
Experimental results demonstrate that RUAL achieves strong overall performance and maintains stable prediction results under missing-modality and Gaussian-noise conditions, validating the effectiveness and robustness of the proposed framework for multimodal sentiment analysis.
