Year: 2025
Type: article
Abstract: The performance of Multimodal Sentiment Analysis (MSA) is commonly hindered by two major bottlenecks: the complexity and redundancy associated with supervised feature disentanglement and the coarse granularity of static fusion mechanisms. To systematically address these challenges, a novel framework, the Decoupled Multi-modal Binding Transformer (DMBT), is proposed. The framework first introduces an Unsupervised Semantic Disentanglement (USD) module, which resolves the issue of complex redundancy by cleanly separating features into modality-common and modality-specific components in a lightweight, parameter-free manner. Subsequently, to tackle the challenge of coarse-grained fusion, a Gated Interaction and Fusion Transformer (GIFT) is constructed as the core engine. The exceptional performance of GIFT is driven by two synergistic components. The first is a Multi-modal Binding Transposed Attention (MBTA) that employs a hybrid convolutional and attention model to concurrently perceive both global context and local fine-grained features, and then a Dynamic Fusion Gate (DFG) that performs final, adaptive decision-making by re-weighting all deeply enhanced representations. Extensive experiments on the CMU-MOSI and CMU-MOSEI benchmarks demonstrate that the proposed DMBT framework surpasses existing state-of-the-art models across all key evaluation metrics. The efficacy of each innovative component is further validated through comprehensive ablation studies.
Source: Electronics
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G2961773516
OpenAlex ID: W4415818084