Summary of Alignmamba: Enhancing Multimodal Mamba with Local and Global Cross-modal Alignment, by Yan Li et al.
AlignMamba: Enhancing Multimodal Mamba with Local and Global Cross-modal Alignment
by Yan Li, Yifei Xing, Xiangyuan Lan, Xin Li, Haifeng Chen, Dongmei Jiang
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers develop a new approach called AlignMamba that efficiently models relationships between different data modalities, such as text and images, for multimodal representation fusion. The authors build upon Transformer-based methods but address their limitations by introducing a local cross-modal alignment module based on Optimal Transport. This module learns token-level correspondences between modalities, enabling more effective modeling of inter-modal relationships. The approach also includes a global alignment loss to ensure consistency across different distributions. Experiments demonstrate the effectiveness and efficiency of AlignMamba for both complete and incomplete multimodal fusion tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new method called AlignMamba that helps computers understand how different types of data, like text and images, are related. This is important because many real-world problems involve multiple types of data. The authors use an idea called Optimal Transport to create a special module that connects the dots between different pieces of information from different sources. They also add a second part to their method that makes sure all the information is working together smoothly. The team tested their approach and found it worked well for combining both complete and incomplete sets of data. |
Keywords
» Artificial intelligence » Alignment » Token » Transformer
