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Multimodal Sentiment Analysis (MSA) leverages heterogeneous modalities, such as language, vision, and audio, to enhance the understanding of human sentiment. While existing models often focus on extracting shared information across modalities or directly fusing heterogeneous modalities, such approaches can introduce redundancy and conflicts due to equal treatment of all modalities and the mutual transfer of information between modality pairs. To address these issues, we propose a Disentangled-Language-Focused (DLF) multimodal representation learning framework, which incorporates a feature disentanglement module to separate modality-shared and modality-specific information. To further reduce redundancy and enhance language-targeted features, four geometric measures are introduced to refine the disentanglement process. A Language-Focused Attractor (LFA) is further developed to strengthen language representation by leveraging complementary modality-specific information through a language-guided cross-attention mechanism. The framework also employs hierarchical predictions to improve overall accuracy. Extensive experiments on two popular MSA datasets, CMU-MOSI and CMU-MOSEI, demonstrate the significant performance gains achieved by the proposed DLF framework. Comprehensive ablation studies further validate the effectiveness of the feature disentanglement module, language-focused attractor, and hierarchical predictions. 

In multimodal machine learning, effectively addressing the missing modality scenario is crucial for improving performance in downstream tasks such as in medical contexts where data may be incomplete. Although some attempts have been made to retrieve embeddings for missing modalities, two main bottlenecks remain: (1) the need to consider both intra- and inter-modal context, and (2) the cost of embedding selection, where embeddings often lack modality-specific knowledge. To address this, the authors propose MoE-Retriever, a novel framework inspired by Sparse Mixture of Experts (SMoE). MoE-Retriever defines a supporting group for intra-modal inputs—samples that commonly lack the target modality—by selecting samples with complementary modality combinations for the target modality. This group is integrated with inter-modal inputs from different modalities of the same sample, establishing both intra- and inter-modal contexts. These inputs are processed by Multi-Head Attention to generate context-aware embeddings, which serve as inputs to the SMoE Router that automatically selects the most relevant experts (embedding candidates). Comprehensive experiments on both medical and general multimodal datasets demonstrate the robustness and generalizability of MoE-Retriever, marking a significant step forward in embedding retrieval methods for incomplete multimodal data. 

In multimodal machine learning, effectively addressing the missing modality scenario is crucial for improving performance in downstream tasks such as in medical contexts where data may be incomplete. Although some attempts have been made to effectively retrieve embeddings for missing modalities, two main bottlenecks remain: the consideration of both intra- and inter-modal context, and the cost of embedding selection, where embeddings often lack modality-specific knowledge. In response, we propose MoE-Retriever, a novel framework inspired by the design principles of Sparse Mixture of Experts (SMoE). First, MoE-Retriever samples the relevant data from modality combinations, using a so-called supporting group to construct intra-modal inputs while incorporating inter-modal inputs. These inputs are then processed by Multi-Head Attention, after which the SMoE Router automatically selects the most relevant expert, i.e., the embedding candidate to be retrieved. Comprehensive experiments on both medical and general multimodal datasets demonstrate the robustness and generalizability of MoE-Retriever, marking a significant step forward in embedding retrieval methods for incomplete multimodal data.