Summary of Domain-aware Fine-tuning Of Foundation Models, by Ugur Ali Kaplan et al.
Domain-Aware Fine-Tuning of Foundation Models
by Ugur Ali Kaplan, Margret Keuper, Anna Khoreva, Dan Zhang, Yumeng Li
First submitted to arxiv on: 3 Jul 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 investigates the zero-shot domain adaptation capabilities of foundation models (FMs) in computer vision. Specifically, it explores how different backbone architectures and novel components leveraging textual embeddings can improve FMs’ performance under domain shift. The proposed approach, called Domino, incorporates domain-aware normalization during fine-tuning to make FM models more robust and adaptable to unseen domains. By comparing various backbone architectures and introducing novel domain-aware components, this research aims to bridge the gap in FMs’ performance across different domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well computer vision models can adapt to new environments or situations without any additional training. Right now, these models are really good at recognizing things like objects and scenes when they’re shown images from the same type of environment they were trained on. But what if you want them to work just as well in a completely different environment? That’s what this research is all about: figuring out how to make computer vision models more flexible so they can adapt quickly and accurately to new situations. |
Keywords
* Artificial intelligence * Domain adaptation * Fine tuning * Zero shot
