Summary of Adapting Large Multimodal Models to Distribution Shifts: the Role Of In-context Learning, by Guanglin Zhou and Zhongyi Han and Shiming Chen and Biwei Huang and Liming Zhu and Salman Khan and Xin Gao and Lina Yao
Adapting Large Multimodal Models to Distribution Shifts: The Role of In-Context Learning
by Guanglin Zhou, Zhongyi Han, Shiming Chen, Biwei Huang, Liming Zhu, Salman Khan, Xin Gao, Lina Yao
First submitted to arxiv on: 20 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper explores the effectiveness of in-context learning (ICL) for enhancing large multimodal models’ (LMMs) adaptability, particularly in healthcare. To address limitations in pre-trained vision encoders under distribution shift scenarios, a novel method called InvariantSelectPR is proposed. This method leverages Class-conditioned Contrastive Invariance (CCI) to improve the discriminative capabilities of pre-trained vision encoders and ensure invariance to domain-specific variations. The authors demonstrate that InvariantSelectPR substantially improves the adaptability of LMMs, achieving significant performance gains on benchmark datasets, such as a 34.2% increase in accuracy for Camelyon17 and a 16.9% increase for HAM10000 compared to zero-shot performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can make big computer models better at understanding new information, especially in areas like healthcare. Currently, these models are really good at doing general tasks but need help when dealing with specific topics. The researchers came up with a new way to teach these models using what they already know, called InvariantSelectPR. This method helps the models learn to recognize and understand important details even when the information is presented differently than before. The results show that this new approach can significantly improve how well the models perform on specific tasks, making them more useful for real-world applications. |
Keywords
» Artificial intelligence » Zero shot
