Summary of The Power Of Many: Multi-agent Multimodal Models For Cultural Image Captioning, by Longju Bai et al.
The Power of Many: Multi-Agent Multimodal Models for Cultural Image Captioning
by Longju Bai, Angana Borah, Oana Ignat, Rada Mihalcea
First submitted to arxiv on: 18 Nov 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 Medium Difficulty summary: Large Multimodal Models (LMMs) have achieved impressive performance across various multimodal tasks. However, their effectiveness in cross-cultural contexts remains limited due to the predominantly Western-centric nature of most data and models. The paper introduces MosAIC, a Multi-Agent framework that enhances cross-cultural Image Captioning using LMMs with distinct cultural personas. The study provides a dataset of culturally enriched image captions in English for images from China, India, and Romania across three datasets: GeoDE, GD-VCR, CVQA. A culture-adaptable metric is proposed to evaluate cultural information within image captions. The multi-agent interaction outperforms single-agent models across different metrics, offering valuable insights for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper looks at how big language models do on tasks that involve understanding and describing images from different cultures. Right now, these models are not very good at this because most of the data they were trained on is from Western countries. The researchers created a new system called MosAIC that helps them understand images better by giving each model its own cultural persona. They also made a dataset with image captions in English for pictures from China, India, and Romania. The team developed a way to measure how well the models do at including cultural information in their captions. Surprisingly, when multiple models work together, they can do even better than one model alone. |
Keywords
» Artificial intelligence » Image captioning
