Summary of Vision-language Model Fine-tuning Via Simple Parameter-efficient Modification, by Ming Li et al.
Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification
by Ming Li, Jike Zhong, Chenxin Li, Liuzhuozheng Li, Nie Lin, Masashi Sugiyama
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Robotics (cs.RO)
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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 research paper presents a new perspective on fine-tuning Vision-Language Models (VLMs), particularly the CLIP model. Contrary to the common belief that fine-tuning VLMs with few-shot samples corrupts pre-trained knowledge, the authors propose a simple yet effective method called ClipFit to fine-tune CLIP without introducing extra parameters. The authors demonstrate that by only fine-tuning specific bias terms and normalization layers, ClipFit can improve zero-shot CLIP’s performance by 7.27% average harmonic mean accuracy. Furthermore, the paper conducts extensive experimental analyses to understand how fine-tuning in ClipFit affects internal parameters and representations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper shows that it’s possible to improve a machine learning model called CLIP without adding new information. The authors tested different ways of adjusting the model and found that changing specific parts of the model can make it perform better. They call this new way of adjusting the model “ClipFit” and show that it works by testing it on several tasks. This is important because it means that researchers can use existing models in new ways to get better results. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Machine learning » Zero shot
