Summary of Data-efficient Contrastive Language-image Pretraining: Prioritizing Data Quality Over Quantity, by Siddharth Joshi et al.
Data-Efficient Contrastive Language-Image Pretraining: Prioritizing Data Quality over Quantity
by Siddharth Joshi, Arnav Jain, Ali Payani, Baharan Mirzasoleiman
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 A newly proposed method for selecting subsets of training data for Contrastive Language-Image Pre-training (CLIP) models achieves remarkable zero-shot generalization performance. The approach, called CLIPCOV, selects data that closely preserves the cross-covariance of images and captions, resulting in superior generalization performance compared to increasing pre-training volume or using large-scale datasets. In experiments on ConceptualCaptions3M and ConceptualCaptions12M, CLIPCOV subsets outperform baselines by 2.7x and 1.4x on ImageNet and its shifted versions, as well as achieving higher average accuracy across 11 downstream datasets. The proposed method is theoretically rigorous and demonstrated to be effective in improving the performance of CLIP models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Contrastive Language-Image Pre-training (CLIP) is a way for computers to learn from lots of images and text together. Right now, this requires a huge amount of data. Researchers want to know how to pick out the most important parts of that data so it’s more useful. They came up with a new method called CLIPCOV that helps them do just that. This method looks at how well certain images and captions match each other, and picks the ones that are most similar. The results show that this method is very good at helping CLIP models perform well on many different tasks. |
Keywords
* Artificial intelligence * Generalization * Zero shot
