--- library_name: transformers license: mit language: - en tags: - retrieval - multi-modal - knowledge-based visual question answering - FLMR - PreFLMR --- # FLMR model card FLMR is an open-source model for multimodal knowledge retrieval. It is a transformer-based model that uses a combination of text and image inputs to retrieve relevant documents from a large corpus. ## Model Details ### Model Description - **Model type:** FLMRModelForRetrieval - **Language(s) (NLP):** English - **License:** MIT License ### Paper and resources for more detail - **Blog Post for quick overview:** https://www.jinghong-chen.net/fined-grained-late-interaction-multimodal-retrieval-flmr/ - **Paper:** https://openreview.net/forum?id=IWWWulAX7g - **Repository:** https://github.com/LinWeizheDragon/FLMR ## Uses ### Direct Use This model can be used directly to retrieve documents from a large corpus using a combination of text and image input queries. The retrieval usage can be found in the [official implementation](https://github.com/LinWeizheDragon/FLMR). ### Downstream Use This model can be used combined with language models to create a retrieval-augmented language model. The use for Knowledge-based VQA can be found in [RAVQA](https://github.com/linweizhedragon/retrieval-augmented-visual-question-answering) ## How to Get Started with the Model For details of training, indexing, and performing retrieval, please refer to [here](https://github.com/LinWeizheDragon/FLMR). ## Training datasets The model is pre-trained on 1. Image to Text retrieval: WIT 3. Image & Question to Text retrieval: OKVQA For details on the dataset split and conversion process, please refer to the paper [Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering](https://openreview.net/forum?id=IWWWulAX7g). The processed datasets are: - https://huggingface.co/datasets/BByrneLab/OKVQA_FLMR_preprocessed_data - https://huggingface.co/datasets/BByrneLab/OKVQA_FLMR_preprocessed_GoogleSearch_passages ## Evaluation datasets The model is evaluated on OKVQA, Infoseek, and FVQA. Please find the evaluation results in [the paper](https://openreview.net/forum?id=IWWWulAX7g). ## Citation **BibTeX:** ``` @inproceedings{ lin2023finegrained, title={Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering}, author={Weizhe Lin and Jinghong Chen and Jingbiao Mei and Alexandru Coca and Bill Byrne}, booktitle={Thirty-seventh Conference on Neural Information Processing Systems}, year={2023}, url={https://openreview.net/forum?id=IWWWulAX7g} } ```