FUSE-Net is proposed, a Factorized and Unified Semantic Enhancement framework that decomposes each modality into shared, specific, and noise subspaces and applies contrastive learning, an information-gain constraint, and duality constraints for structured regularization to preserve task-relevant semantics during factorization.
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
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
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
This work proposes an explicit sentiment-guided alignment and multi-dimensional cross-modal mutual supervisionbased model for multimodal sentiment analysis that primarily employs a fine-grained sentiment–saliency directed alignment mechanism and introduces a tripartite strong contrastive learning strategy to mitigate d...
Shi-Shu Qi, Yulei Zhang, Siyang Zhang et al.· Computer Science and Informa...· 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 inform...
Zelong Li, Shu-Hua Lu, Fang Cui et al.· Neural Networks· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
