In this paper, we propose a novel language model guided captioning approach, LAMOC, for knowledge-based visual question answering (VQA). Our approach employs the generated captions by a captioning model as the context of an answer prediction model, which is a Pre-trained Language model (PLM). As the major contribution, we leverage the guidance and feedback of the prediction model to improve the capability of the captioning model. In this way, the captioning model can become aware of the task goal and information need from the PLM. To develop our approach, we design two specific training stages, where the first stage adapts the captioning model to the prediction model (selecting more suitable caption propositions for training) and the second stage tunes the captioning model according to the task goal (learning from feedback of the PLM). Extensive experiments demonstrate the effectiveness of the proposed approach on the knowledge-based VQA task. Specifically, on the challenging A-OKVQA dataset, LAMOC outperforms several competitive zero-shot methods and even achieves comparable results to a fine-tuned VLP model. Our code is publicly available at https://github.com/RUCAIBox/LAMOC.

翻译：本文提出了一种新颖的基于语言模型引导的标题生成方法LAMOC，用于知识驱动的视觉问答（VQA）任务。该方法采用标题生成模型生成的描述作为答案预测模型（即预训练语言模型PLM）的上下文。主要贡献在于，我们利用预测模型的引导与反馈来提升标题生成模型的能力，使其能够感知任务目标与PLM的信息需求。为实现该方法，我们设计了两阶段训练：第一阶段使标题生成模型适配预测模型（筛选更合适的标题命题进行训练），第二阶段根据任务目标微调标题生成模型（通过PLM反馈学习）。大量实验证明了该方法在知识型VQA任务上的有效性。具体而言，在具有挑战性的A-OKVQA数据集上，LAMOC不仅优于多种竞争性零样本方法，甚至达到了与微调后的VLP模型相当的性能。我们的代码已开源发布于https://github.com/RUCAIBox/LAMOC。