Papers › PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers
PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers
Weizhe Lin, Jingbiao Mei, Jinghong Chen, Bill Byrne
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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Datasets
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Retrieval | InfoSeek | PreFLMR | Recall@5 | 62.1 | #1 of 1 | Archive leaderboard | report |
| Visual Question Answering (VQA) | InfoSeek | RA-VQAv2 w/ PreFLMR | Accuracy | 30.65 | #1 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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