Summary of Icc: Quantifying Image Caption Concreteness For Multimodal Dataset Curation, by Moran Yanuka et al.
ICC: Quantifying Image Caption Concreteness for Multimodal Dataset Curation
by Moran Yanuka, Morris Alper, Hadar Averbuch-Elor, Raja Giryes
First submitted to arxiv on: 2 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 proposed paper tackles the issue of noisy data in multimodal learning by introducing a new metric called image caption concreteness. This metric evaluates the concreteness and relevance of text captions without referencing an image, which is crucial for selecting high-quality samples from web-scale datasets. The authors leverage strong foundation models to measure visual-semantic information loss in multimodal representations, demonstrating a strong correlation with human evaluations of concreteness at both single-word and sentence levels. Furthermore, the proposed curation approach complements existing methods, enabling efficient training in resource-constrained settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to clean noisy data used for learning from text and images together. This noise can make it hard to train models effectively. The solution is called image caption concreteness, which helps measure how concrete or relevant the text is without seeing an image. The authors use strong AI models to test this idea and show that it works well with human evaluations. They also compare their approach to other methods and find that it can help improve training on limited resources. |
