The paper introduces Q&A Prompts, a novel framework that improves reasoning in Visual Question Answering (VQA) tasks requiring diverse world knowledge. By generating question-answer prompts and encoding them with a visual-aware prompting module, significant improvements in performance are achieved on challenging VQA datasets. The method effectively bridges the gap between perception and reasoning by collecting rich visual clues from images.
The study explores the effectiveness of Q&A prompts through experiments on A-OKVQA and OK-VQA datasets, showcasing substantial advancements over state-of-the-art methods. Extensive ablation studies demonstrate the importance of various components in the visual-aware prompting module. Additionally, qualitative analyses highlight how Q&A prompts contribute to accurate reasoning in complex VQA scenarios.
Limitations include potential biases in data and limitations in fine-grained counting and Optical Character Recognition. Future work aims to address these issues for further enhancement of model reasoning capabilities.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Haibi Wang,W... at arxiv.org 03-07-2024
https://arxiv.org/pdf/2401.10712.pdfDeeper Inquiries