Summary of Pyramid Coder: Hierarchical Code Generator For Compositional Visual Question Answering, by Ruoyue Shen et al.
Pyramid Coder: Hierarchical Code Generator for Compositional Visual Question Answering
by Ruoyue Shen, Nakamasa Inoue, Koichi Shinoda
First submitted to arxiv on: 30 Jul 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 In this paper, researchers tackle the challenge of developing programmatic visual question answering (VQA) models that can generate accurate answers to natural language questions based on visual input. They introduce PyramidCoder, a novel prompting framework for PVQA models that utilizes large language models (LLMs) and pre-defined prompts to enable complex visual reasoning. The framework consists of three hierarchical levels: query rephrasing, code generation, and answer aggregation. Compared to state-of-the-art PVQA models, the proposed approach improves accuracy on various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand natural language questions by giving them a special set of instructions. This is important because it allows computers to provide more accurate answers to questions that require looking at pictures and understanding what’s happening in them. The researchers developed a new way to teach these computer models called PyramidCoder, which has three steps: rewriting the question, generating code for the computer, and combining the answers. This approach works well on different datasets and can be used with many different types of language models. |
Keywords
» Artificial intelligence » Prompting » Question answering
