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        <title>PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers</title>
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        <namePart type="given">Weizhe</namePart>
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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.</abstract>
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%0 Conference Proceedings
%T PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers
%A Lin, Weizhe
%A Mei, Jingbiao
%A Chen, Jinghong
%A Byrne, Bill
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F lin-etal-2024-preflmr
%X 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.
%R 10.18653/v1/2024.acl-long.289
%U https://aclanthology.org/2024.acl-long.289
%U https://doi.org/10.18653/v1/2024.acl-long.289
%P 5294-5316

Markdown (Informal)

[PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers](https://aclanthology.org/2024.acl-long.289) (Lin et al., ACL 2024)

• PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers (Lin et al., ACL 2024)

ACL

• Weizhe Lin, Jingbiao Mei, Jinghong Chen, and Bill Byrne. 2024. PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5294–5316, Bangkok, Thailand. Association for Computational Linguistics.