Summary of Bridging the Visual Gap: Fine-tuning Multimodal Models with Knowledge-adapted Captions, by Moran Yanuka et al.
Bridging the Visual Gap: Fine-Tuning Multimodal Models with Knowledge-Adapted Captions
by Moran Yanuka, Assaf Ben Kish, Yonatan Bitton, Idan Szpektor, Raja Giryes
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 This paper investigates the challenges of training vision-language models (VLMs) with long, detailed image captions. Small-scale VLMs often struggle to balance the richness of these captions with the risk of hallucinating content during fine-tuning. To address this issue, the authors propose an evaluation framework called Decomposed NLI (DNLI), which assesses generated captions at a fine-grained level by breaking them down into individual propositions. The study finds that simply reducing caption complexity or employing standard data curation techniques does not effectively resolve the problem. Instead, the authors introduce Knowledge Adapted (KnowAda) fine-tuning, a data-centric approach that automatically adapts training data with the model’s existing knowledge and visual understanding. KnowAda minimizes hallucinations while preserving high descriptiveness. The paper validates this approach across several small-scale VLMs and dense caption datasets, demonstrating that KnowAda outperforms various baselines in both automatic metrics and human evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how to train computer models called vision-language models (VLMs) with long descriptions of images. These models often struggle to balance the amount of detail they include with their ability to make things up during training. To solve this problem, the researchers created a way to evaluate the quality of these descriptions by looking at each part separately. They found that making the descriptions shorter or using special techniques to clean up the data didn’t fix the issue. Instead, they developed a new approach called Knowledge Adapted fine-tuning, which helps the model learn from its existing knowledge and visual understanding. This approach reduces the likelihood of the model making things up while still including plenty of detail. The researchers tested this approach with different models and datasets and found that it worked better than other methods. |
Keywords
» Artificial intelligence » Fine tuning » Likelihood
