Summary of Fivl: a Framework For Improved Vision-language Alignment Through the Lens Of Training, Evaluation and Explainability, by Estelle Aflalo et al.
FiVL: A Framework for Improved Vision-Language Alignment through the Lens of Training, Evaluation and Explainability
by Estelle Aflalo, Gabriela Ben Melech Stan, Tiep Le, Man Luo, Shachar Rosenman, Sayak Paul, Shao-Yen Tseng, Vasudev Lal
First submitted to arxiv on: 19 Dec 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 proposes a novel method, FiVL, to train Large Vision Language Models (LVLMs) for enhanced visual grounding in multimodal reasoning tasks. The authors argue that current LVLMs rely too heavily on linguistic priors and neglect the importance of visual information. They introduce a new training task and dataset designed specifically to address this issue. The method is evaluated through three approaches: a novel training task, benchmarking the model’s ability to use images as evidence, and identifying attention heads with strong vision-language alignment. This work aims to improve the performance of LVLMs in tasks like visual question answering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Vision Language Models (LVLMs) are great at understanding words and pictures together! But they often rely too much on what they already know from text instead of using the picture itself. The researchers wanted to fix this by creating a new way to train LVLMs that helps them use images more effectively. They made a special dataset and three tests to see how well it works. |
Keywords
» Artificial intelligence » Alignment » Attention » Grounding » Question answering
