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arXiv · 2402.08327

PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers

Weizhe Lin·Jingbiao Mei·Jinghong Chen·Bill Byrne
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Abstract

Large Multimodal Models (LMMs) excel in natural language and visual understanding but are challenged by exacting tasks such as Knowledge-based Visual Question Answering (KB-VQA) which involve the retrieval of relevant information from document collections to use in shaping answers to questions. We present an extensive training and evaluation framework, M2KR, for KB-VQA. M2KR contains a collection of vision and language tasks which we have incorporated into a single suite of benchmark tasks for training and evaluating general-purpose multi-modal retrievers. We use M2KR to develop PreFLMR, a pre-trained version of the recently developed Fine-grained Late-interaction Multi-modal Retriever (FLMR) approach to KB-VQA, and we report new state-of-the-art results across a range of tasks. We also present investigations into the scaling behaviors of PreFLMR intended to be useful in future developments in general-purpose multi-modal retrievers.

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Weizhe Lin, Jingbiao Mei, Jinghong Chen, Bill Byrne. 2024-02-13. PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers. https://arxiv.org/abs/2402.08327

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