Summary of Jointly Modeling Inter- & Intra-modality Dependencies For Multi-modal Learning, by Divyam Madaan et al.
Jointly Modeling Inter- & Intra-Modality Dependencies for Multi-modal Learning
by Divyam Madaan, Taro Makino, Sumit Chopra, Kyunghyun Cho
First submitted to arxiv on: 27 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 tackle the problem of supervised multi-modal learning, where they aim to map multiple types of data (e.g., images, audio, text) to a target label. Previous studies have focused on either capturing the relationships between different data types and the label or the relationships within a single type of data and the label. The authors argue that these approaches may not be optimal in general and propose a new framework called inter- & intra-modality modeling (I2M2), which integrates both types of relationships to improve accuracy. They demonstrate the effectiveness of their approach using real-world healthcare and vision-and-language datasets with state-of-the-art models, outperforming traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching machines to understand multiple kinds of data, like pictures and words, together. The authors want to make sure these machines learn from all the different types of data, not just one or the other. They think that by learning from both how the different types of data relate to each other and how they relate within themselves, they can get better results. To do this, they create a new way to look at this problem using special kinds of computer models. This helps them make more accurate predictions about things like what’s in a picture or what someone is saying. |
Keywords
» Artificial intelligence » Multi modal » Supervised
