Summary of Disentangling Knowledge-based and Visual Reasoning by Question Decomposition in Kb-vqa, By Elham J. Barezi et al.
Disentangling Knowledge-based and Visual Reasoning by Question Decomposition in KB-VQA
by Elham J. Barezi, Parisa Kordjamshidi
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 tackles the Knowledge-Based visual question-answering problem, where models must connect a given question to relevant image information. Current approaches using Large Language Models (LLMs) and question-dependent captioners struggle with multi-hop questions. Our study shows that replacing complex questions with simpler ones enhances image comprehension and improves performance by up to 2% on three well-known VQA datasets: OKVQA, A-OKVQA, and KRVQA. We decompose questions into visual and non-visual components, using a captioner for the former and LLMs as a general knowledge source for the latter. This approach demonstrates the positive impact of breaking down complex questions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps machines better understand images based on what you ask them. Right now, machines are not great at answering questions that require looking at multiple parts of an image. The researchers found that by asking simpler questions first, they can get a better understanding of the image and answer the question more accurately. They tested their idea on three big datasets and saw improvements of up to 2%. This is important because it can help machines be more helpful in real-life situations. |
Keywords
» Artificial intelligence » Question answering
