Multi-Modal Representation via Contrastive Learning with Attention Bottleneck Fusion and Attentive Statistics...

Area of Science:

• Artificial Intelligence
• Machine Learning
• Data Science

Background:

• Multimodal data integration is crucial but existing methods often miss hierarchical relationships.
• Previous techniques focus on shallow or high-level features, limiting fine-grained statistical analysis.

Purpose of the Study:

• To propose a novel approach for dense multimodal representation integration.
• To improve the comprehension and characterization of multimodal data through holistic feature analysis.

Main Methods:

• Computing image features' means and standard deviations for dense representation.
• Utilizing a Transformer-based fusion encoder to capture global feature variations.
• Incorporating a contrastive loss function to discover cross-modal shared information.

Main Results:

• The proposed method achieves significant performance improvements on multimodal sentiment analysis tasks.
• Demonstrated superior efficacy compared to existing state-of-the-art approaches.
• Validated on three widely used multimodal sentiment analysis datasets.

Conclusions:

• The novel approach effectively integrates multimodal information by leveraging global statistics and advanced fusion techniques.
• This method offers a more comprehensive understanding of multimodal data, advancing the field of multimodal learning.