Summary of Can Mllms Perform Text-to-image In-context Learning?, by Yuchen Zeng et al.
Can MLLMs Perform Text-to-Image In-Context Learning?
by Yuchen Zeng, Wonjun Kang, Yicong Chen, Hyung Il Koo, Kangwook Lee
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 abstract presents a study on extending In-Context Learning (ICL) to its multimodal counterpart, focusing on Text-to-Image ICL (T2I-ICL). The authors formally define the T2I-ICL task and introduce CoBSAT, a benchmark dataset comprising ten tasks. They evaluate six state-of-the-art Multimodal Large Language Models (MLLMs) on this dataset, revealing challenges due to multimodality and image generation. Strategies like fine-tuning and Chain-of-Thought prompting improve performance, highlighting the importance of T2I-ICL in MLLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary T2I-ICL is a new way for computers to learn from text and images together. The researchers made a special dataset called CoBSAT that has ten tasks for computers to practice this skill. They tested six popular AI models on this dataset and found out that they struggle with T2I-ICL because it’s hard to understand both text and images at the same time. To make things better, they tried fine-tuning the models and giving them more information about what to do. This made the models perform much better. |
Keywords
* Artificial intelligence * Fine tuning * Image generation * Prompting
