Summary of Right This Way: Can Vlms Guide Us to See More to Answer Questions?, by Li Liu et al.
Right this way: Can VLMs Guide Us to See More to Answer Questions?
by Li Liu, Diji Yang, Sijia Zhong, Kalyana Suma Sree Tholeti, Lei Ding, Yi Zhang, Leilani H. Gilpin
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 A new study investigates how Vision Language Models (VLMs) can indicate when visual information is insufficient for answering a question, mimicking human behavior. Current VLMs typically generate one-shot responses without evaluating the sufficiency of available information. To address this gap, researchers introduce a human-labeled dataset as a benchmark for assessing VLM performance in Visual Question Answering (VQA) scenarios. They also present an automated framework generating synthetic training data by simulating “where to know” scenarios. Fine-tuning mainstream VLMs with this synthetic data leads to significant performance improvements. This study demonstrates the potential to bridge the gap between information assessment and acquisition in VLMs, bringing their performance closer to humans. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VLMs are artificial intelligence models that answer questions based on visual information. Humans can tell when they have enough information to answer a question or need more details. The researchers wanted to see if VLMs could do the same thing. They created a special dataset and an automated way to generate training data to help VLMs learn how to identify when they don’t have enough information. This study shows that by using this new approach, VLMs can improve their performance in answering visual questions. |
Keywords
* Artificial intelligence * Fine tuning * One shot * Question answering * Synthetic data
