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Title: Pre-Training Multi-Modal Dense Retrievers for Outside-Knowledge Visual Question Answering
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 OKVQA systems is to retrieve relevant documents for the given multimodal query. Current state-of-the-art dense retrieval model for this task uses an asymmetric 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. Additionally, the proposed pre-training approach exhibits a good ability in zero-shot retrieval scenarios.  more » « less
Award ID(s):
2106282
PAR ID:
10434902
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of The 13th International Conference on the Theory of Information Retrieval (ICTIR 2023)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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