This paper studies a category of visual question answering tasks, in which
accessing external knowledge is necessary for answering the questions. This
category is called outside-knowledge visual question answering (OK-VQA). A
major step in developing OK-VQA systems is to retrieve relevant documents for
the given multi-modal query. Current state-of-the-art asymmetric dense
retrieval model for this task uses an architecture with a multi-modal query
encoder and a uni-modal document encoder. Such an architecture requires a large
amount of training data for effective performance. We propose an automatic data
generation pipeline for pre-training passage retrieval models for OK-VQA tasks.
The proposed approach leads to 26.9% Precision@5 improvements compared to the
current state-of-the-art asymmetric architecture. Additionally, the proposed
pre-training approach exhibits a good ability in zero-shot retrieval scenarios.

本文研究了外部知识可视化问题回答的一类任务，提出了一种自动数据生成流程以预训练用于 OK-VQA 任务的通道检索模型，该方法相较于当前最先进的不对称体系结构提供了 26.9％的 Precision@5 改进，并且在零 - shot 检索方案中表现出良好的能力。