Summary of Points: Improving Your Vision-language Model with Affordable Strategies, by Yuan Liu et al.
POINTS: Improving Your Vision-language Model with Affordable Strategies
by Yuan Liu, Zhongyin Zhao, Ziyuan Zhuang, Le Tian, Xiao Zhou, Jie Zhou
First submitted to arxiv on: 7 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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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 proposed contributions aim to address critical issues in vision-language models, including transparency in proprietary models, under-explored pre-training data in open-source works, and diminishing returns from fine-tuning. A robust baseline model is trained using the latest advancements in vision-language models, with effective improvements and comprehensive ablation and validation for each technique. The pre-training data is filtered using perplexity, selecting the lowest perplexity data for training, allowing for competitive performance on a curated 1M dataset. Additionally, visual instruction tuning uses model soup when adding more datasets yields marginal improvements. These innovations result in a 9B parameter model that performs competitively with state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Vision-language models are making progress in tasks like recognizing characters and solving problems. However, there are some big issues to fix. Some models don’t tell us how they work, while others use too much data without understanding what’s most important. To solve these problems, researchers propose three new ways: 1) Train a good model that works well with different techniques and test each one thoroughly. 2) Use a special method to select the best data for training, rather than just adding more data. 3) When fine-tuning models, only add more data if it really helps improve performance. These new methods help create a powerful model with many parameters that performs as well as top models. |
Keywords
» Artificial intelligence » Fine tuning » Instruction tuning » Perplexity
