Summary of Renaissance: Investigating the Pretraining Of Vision-language Encoders, by Clayton Fields et al.
Renaissance: Investigating the Pretraining of Vision-Language Encoders
by Clayton Fields, Casey Kennington
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 tackles the challenges in designing and training vision-language models, which have seen a surge in recent years. The authors conduct a meta-analysis to investigate best practices in pretraining vision-language encoders. They demonstrate that freezing large parts of these models during pretraining can save significant computational resources without sacrificing downstream performance. Additionally, they explore the effect of using vision-based versus text-based transformers and introduce Renaissance, a flexible platform for creating, training, and evaluating transformer encoders. This platform offers a great deal of flexibility in VL modeling and is available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper answers questions about how to design and train special computers that can understand both pictures and words. The authors looked at many different models and found ways to make them work better without using too much computer power. They also compared using vision-based or text-based models and created a new platform called Renaissance that makes it easy to create, train, and test these models. |
Keywords
» Artificial intelligence » Pretraining » Transformer
