Summary of Supervised Fine-tuning in Turn Improves Visual Foundation Models, by Xiaohu Jiang et al.
Supervised Fine-tuning in turn Improves Visual Foundation Models
by Xiaohu Jiang, Yixiao Ge, Yuying Ge, Dachuan Shi, Chun Yuan, Ying Shan
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed two-stage method, ViSFT (Vision SFT), enhances the generation of vision foundation models by performing fine-grained supervised training on in-domain tasks and testing on out-of-domain benchmarks. This approach leverages the strengths of CLIP’s pretraining while overcoming scalability challenges posed by region-level visual learning. The authors demonstrate the effectiveness of ViSFT, achieving improvements across various out-of-domain benchmarks including vision and vision-linguistic scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ViSFT is a new way to improve computer vision models. It works by training the model on specific tasks related to what it’s good at, and then testing how well it does on other tasks. This helps the model learn more about what it sees and improves its ability to understand images. The authors used this method with a big model that had over 4 billion parameters and showed it worked well on different benchmarks. |
Keywords
» Artificial intelligence » Pretraining » Supervised
