Summary of Multilingual Diversity Improves Vision-language Representations, by Thao Nguyen et al.
Multilingual Diversity Improves Vision-Language Representations
by Thao Nguyen, Matthew Wallingford, Sebastin Santy, Wei-Chiu Ma, Sewoong Oh, Ludwig Schmidt, Pang Wei Koh, Ranjay Krishna
First submitted to arxiv on: 27 May 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 The paper explores the benefits of using massive web-crawled image-text datasets with a focus on non-English samples in multimodal learning. By questioning the practice of predominantly using English-centric data, the authors show that multilingual data can enrich training sets and improve model performance. The study translates multilingual pairs to English and re-filtering them, resulting in a dataset used for pre-training. This approach outperforms English-only or dominated datasets on various tasks, including ImageNet, distribution shifts, image-English-text retrieval, and the DataComp benchmark. Additionally, the paper finds that English and non-English data differ significantly in both image and text spaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how using lots of pictures with words from different languages can help computers learn better. The authors think that right now, most computer vision training sets are made up of mostly English words and pictures, which isn’t fair. They want to show that using more pictures and words from other languages can actually make the computers smarter. To do this, they took lots of pictures and words from the internet, translated them into English, and used them to train a computer model. This new approach worked better than just using English words and pictures on many different tasks. |
