Summary of Quantifying the Gaps Between Translation and Native Perception in Training For Multimodal, Multilingual Retrieval, by Kyle Buettner et al.
Quantifying the Gaps Between Translation and Native Perception in Training for Multimodal, Multilingual Retrieval
by Kyle Buettner, Adriana Kovashka
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 investigates multilingual vision-language models that can adapt to different linguistic and cultural contexts. Specifically, it examines the impact of language perception on image captions and proposes caption augmentation strategies to improve model flexibility. The study reveals performance gaps between native German and machine- or human-translated German captions, highlighting the need for more inclusive training data. The proposed approach achieves mean recall improvements, but further work is required to fully address these gaps. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how image recognition models can understand different languages and cultures. Right now, most models are trained on English captions and might not do as well with captions from other languages or cultures. This study shows that even when machine-translating or human-translating captions from one language to another, the model’s performance drops significantly. To fix this, the authors suggest ways to make the captions more diverse and challenging for the model. While some improvements are seen, there is still a lot of work needed to make these models truly flexible. |
Keywords
» Artificial intelligence » Recall
