We investigate knowledge retrieval with multi-modal queries, i.e. queries
containing information split across image and text inputs, a challenging task
that differs from previous work on cross-modal retrieval. We curate a new
dataset called ReMuQ for benchmarking progress on this task. ReMuQ requires a
system to retrieve knowledge from a large corpus by integrating contents from
both text and image queries. We introduce a retriever model ``ReViz'' that can
directly process input text and images to retrieve relevant knowledge in an
end-to-end fashion without being dependent on intermediate modules such as
object detectors or caption generators. We introduce a new pretraining task
that is effective for learning knowledge retrieval with multimodal queries and
also improves performance on downstream tasks. We demonstrate superior
performance in retrieval on two datasets (ReMuQ and OK-VQA) under zero-shot
settings as well as further improvements when finetuned on these datasets.

本文介绍了一个新的数据集 ReMuQ，针对跨媒体检索的任务，提出了一个直接处理文本和图像输入的 Retriever 模型 `ReViz`，并引入了一个新的预训练任务，实现了对多模态查询的知识检索，并在两个数据集上取得了优秀的检索效果。