Summary of Knowledge Generation For Zero-shot Knowledge-based Vqa, by Rui Cao and Jing Jiang
Knowledge Generation for Zero-shot Knowledge-based VQA
by Rui Cao, Jing Jiang
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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 paper proposes a novel approach to knowledge-based visual question answering (K-VQA), which leverages pre-trained large language models (LLMs) as both a knowledge source and a zero-shot QA model. The method generates knowledge from the LLM and incorporates it into the K-VQA process, allowing for interpretable results. In contrast to previous solutions that rely on external knowledge bases and supervised learning, this approach uses a knowledge-generation-based framework to achieve promising results on two K-VQA benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to answer visual questions by using big language models. Instead of relying on outside sources or training a special model, it generates the needed information from the language model itself. This helps make the answers easier to understand and improves performance on visual question answering tasks. The approach is tested on two sets of questions and shows better results than previous methods that don’t use this kind of knowledge generation. |
Keywords
» Artificial intelligence » Language model » Question answering » Supervised » Zero shot
