Summary of Q&a Prompts: Discovering Rich Visual Clues Through Mining Question-answer Prompts For Vqa Requiring Diverse World Knowledge, by Haibo Wang et al.
Q&A Prompts: Discovering Rich Visual Clues through Mining Question-Answer Prompts for VQA requiring Diverse World Knowledge
by Haibo Wang, Weifeng Ge
First submitted to arxiv on: 19 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research paper proposes a novel approach to developing AI models capable of answering complex visual questions that require advanced reasoning abilities. The authors argue that by collecting rich visual clues from images, they can improve the accuracy of image recognition, question understanding, and knowledge recall, ultimately leading to better answers. To achieve this, they develop a method called Q&A Prompts, which involves training a visual question generation model on image-answer pairs and then using an image tagging model to identify relevant instances. The generated questions are then used as prompts for pre-trained multi-modal large language models to reason out final answers. Experimental results show that their approach achieves significant improvements over state-of-the-art methods on challenging datasets such as OK-VQA and A-OKVQA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us develop AI models that can answer complex questions about images. Right now, these models are good at recognizing things in pictures, but they struggle to understand what’s going on in the image and answer questions correctly. The authors have a new idea called Q&A Prompts, which uses special training data to teach the model how to ask better questions based on the image. They then use this trained model to generate prompts for another AI model that can reason out answers. This approach leads to big improvements in answering difficult visual question-answering tasks. |
Keywords
» Artificial intelligence » Multi modal » Question answering » Recall
