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Title: End-to-end Knowledge Retrieval with Multi-modal Queries
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.  more » « less
Award ID(s):
1816039
NSF-PAR ID:
10432858
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
61st Annual Meeting of the Association for Computational Linguistics
Volume:
1
Page Range / eLocation ID:
8573–8589
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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