Summary of In-context Learning Improves Compositional Understanding Of Vision-language Models, by Matteo Nulli et al.
In-Context Learning Improves Compositional Understanding of Vision-Language Models
by Matteo Nulli, Anesa Ibrahimi, Avik Pal, Hoshe Lee, Ivona Najdenkoska
First submitted to arxiv on: 22 Jul 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 This paper explores the limitations of Vision-Language Models (VLMs) in performing compositional image understanding tasks, despite their success in various downstream applications. The authors investigate why VLMs struggle with object bias present in training data and compare contrastive models with generative ones to identify differences in architecture, pre-training data, and training tasks and losses. They also leverage In-Context Learning (ICL) to enhance the ability of VLMs to perform complex reasoning and understanding given an image. The proposed approach outperforms baseline models across multiple compositional understanding datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well computers can understand pictures by combining what they see with what they know about words. Right now, these computer models are really good at doing lots of things like recognizing objects and answering questions. But they struggle to understand complex scenes where many objects are present. The researchers in this paper try to figure out why that is and how to make the computers better at understanding pictures by combining them with words. |
