Summary of Borrowing Treasures From Neighbors: In-context Learning For Multimodal Learning with Missing Modalities and Data Scarcity, by Zhuo Zhi et al.
Borrowing Treasures from Neighbors: In-Context Learning for Multimodal Learning with Missing Modalities and Data Scarcity
by Zhuo Zhi, Ziquan Liu, Moe Elbadawi, Adam Daneshmend, Mine Orlu, Abdul Basit, Andreas Demosthenous, Miguel Rodrigues
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 novel approach to address the challenge of multimodal machine learning with missing modalities in low-data regimes. In this setting, both missing modalities and limited sample size issues are present, making it particularly challenging. The authors introduce retrieval-augmented in-context learning, leveraging the transformer’s ability to learn from available full-modality data. This data-dependent framework demonstrates improved sample efficiency and enhances classification model performance on both full- and missing-modality data across various multimodal learning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem that happens when we don’t have enough information to train machines to do certain jobs. Sometimes, we can get some information, but not all of it. This makes it harder for the machine to learn. The authors came up with a new way to help machines learn even when they’re missing important pieces of information. They tested their method and found that it worked really well, especially when there was very little data available. |
Keywords
* Artificial intelligence * Classification * Machine learning * Transformer
