Pre-Training Multi-Modal Dense Retrievers for Outside-Knowledge Visual
  Question Answering
Alireza Salemi Mahta Rafiee Hamed Zamani
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.