PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers
<|reference_start|>PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers: 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.<|reference_end|>
@article{lin2024preflmr:,
    title={PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers},
    author={Weizhe Lin, Jingbiao Mei, Jinghong Chen, Bill Byrne},
    journal={arXiv preprint arXiv:2402.08327},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08327},
    primaryClass={cs.CL}
}