End-to-end Knowledge Retrieval with Multi-modal Queries
Man Luo Zhiyuan Fang Tejas Gokhale Yezhou Yang Chitta Baral
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.