Summary of Dlf: Disentangled-language-focused Multimodal Sentiment Analysis, by Pan Wang et al.
DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis
by Pan Wang, Qiang Zhou, Yawen Wu, Tianlong Chen, Jingtong Hu
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a Disentangled-Language-Focused (DLF) multimodal representation learning framework for Multimodal Sentiment Analysis (MSA). Traditional MSA models often treat all modalities equally, which can introduce redundancy and conflicts. The DLF framework addresses this by separating modality-shared and modality-specific information using a feature disentanglement module. Four geometric measures are introduced to refine the disentanglement process, ensuring language-targeted features are strengthened. A Language-Focused Attractor (LFA) is developed to leverage complementary modality-specific information through cross-attention. Hierarchical predictions improve overall accuracy. The framework is evaluated on two popular MSA datasets, CMU-MOSI and CMU-MOSEI, demonstrating significant performance gains. Comprehensive ablation studies validate the effectiveness of the feature disentanglement module, language-focused attractor, and hierarchical predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves how computers understand human emotions by combining different types of data, like text, images, and sound. Current methods often treat all these types equally, which can cause problems. The new method, called Disentangled-Language-Focused (DLF), separates the important information from each type of data to make it easier for the computer to understand. This helps the computer focus on the language-based information that is most important for understanding emotions. The paper tests this method on two big datasets and shows that it works much better than existing methods. |
Keywords
» Artificial intelligence » Cross attention » Representation learning
