With the breakthrough of multi-modal large language models, answering complex
visual questions that demand advanced reasoning abilities and world knowledge
has become a much more important testbed for developing AI models than ever.
However, equipping AI models with robust cross-modality reasoning ability
remains challenging since the cognition scheme of humans has not been
understood systematically. In this paper, we believe that if we can collect
visual clues in the given image as much as possible, we will recognize the
image more accurately, understand the question better, recall relevant
knowledge more easily, and finally reason out the answer. We discover these
rich visual clues by mining question-answer pairs in images and sending them
into multi-modal large language models as prompts. We call the proposed method
Q&A Prompts. Specifically, we first use the image-answer pairs and the
corresponding questions in the training set as inputs and outputs to train a
visual question generation model. Then, we use an image tagging model to
identify various instances and send packaged image-tag pairs into the visual
question generation model to generate relevant questions with the extracted
image tags as answers. Finally, we encode these generated question-answer pairs
as prompts with a visual-aware prompting module and send them into pre-trained
multi-modal large language models to reason out the final answers. Experimental
results show that, compared with state-of-the-art methods, our Q&A Prompts
achieves substantial improvements on the challenging visual question answering
datasets requiring reasoning over diverse world knowledge, such as OK-VQA and
A-OKVQA.

我们提出了一种名为 Q&A Prompts 的方法，该方法通过挖掘图像中的问题 - 回答对来发现丰富的视觉线索，并将其作为提示发送给预训练的多模态大型语言模型，以在需要涉及不同世界知识的具有挑战性的视觉问题回答数据集上实现了显著的改进。