Summary of Annotations on a Budget: Leveraging Geo-data Similarity to Balance Model Performance and Annotation Cost, by Oana Ignat et al.
Annotations on a Budget: Leveraging Geo-Data Similarity to Balance Model Performance and Annotation Cost
by Oana Ignat, Longju Bai, Joan Nwatu, Rada Mihalcea
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 recent study reveals that foundation models, despite their impressive performance across various tasks, struggle to generalize well for underrepresented countries due to the geographical and economic imbalance of training data. The majority of this data originates from Western countries, leading to poor results for other regions. To address this issue, researchers propose a cost-effective approach to identify and annotate relevant images from these countries, focusing on topics with visually distinct representations in existing datasets. This method aims to supplement current foundation models’ training data while reducing annotation costs. The proposed methods demonstrate improved model performance and reduced costs when incorporating data from countries with higher visual similarity to the target topics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Foundation models have achieved great results across many tasks, but they struggle to do well for everyone because of the way the data is used to train them. Most of this data comes from Western countries, which means they don’t perform well in other parts of the world. To fix this, researchers want to collect more data from these underrepresented areas, but it’s expensive to label all that data. So, they’re trying to find a way to focus on the most important images and topics in those areas. They think that by doing this, they can make the models better and cheaper to train. |
