New ConsMSA method improves multimodal sentiment analysis with consistency learning

Researchers have developed ConsMSA, a novel approach for multimodal sentiment analysis that addresses semantic inconsistencies across different data types like text, video, and audio. By projecting features into a shared sentiment space, ConsMSA calculates an Intra- and Inter-modality Consistency Score (I²CS). This score is used to regularize latent distributions, adaptively reweight multimodal tokens, and prune redundant data, leading to improved accuracy and efficiency. Experiments on benchmark datasets show that ConsMSA achieves state-of-the-art results even when significantly reducing the number of tokens processed. AI

IMPACT This method could lead to more efficient and accurate sentiment analysis systems by reducing computational costs and improving robustness.

RANK_REASON The item is a research paper detailing a new method for multimodal sentiment analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• alphaXiv
• arXiv
• CatalyzeX
• CH-SIMS
• CMU-MOSEI
• CMU-MOSI
• ConsMSA
• DagsHub
• Gotit.pub
• Hugging Face
• IArxiv
• ScienceCast

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →