PreFLMR model card
PreFLMR 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/preflmr-sota-open-sourced-multi/
- Paper: https://arxiv.org/abs/2402.08327
- Gradio Demo: https://u60544-b8d4-53eaa55d.westx.seetacloud.com:8443/
- Repository: https://github.com/LinWeizheDragon/FLMR
- Project Page: https://preflmr.github.io/
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.
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
How to Get Started with the Model
For details of training, indexing, and performing retrieval, please refer to here.
Training datasets
The model is pre-trained on three types of tasks with a total of nine datasets:
- Image to Text retrieval: WIT, KVQA, and CC3M
- Question to Text retrieval: MSMARCO
- Image & Question to Text retrieval: LLaVA, OVEN, OKVQA, Infoseek and E-VQA
These datasets were converted to retrieval format. For details on the dataset split and conversion process, please refer to the paper PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers. We will release the proprocessed datasets soon.
Evaluation datasets
We evaluate our models on WIT, LLaVA, OVEN, KVQA, IGLUE (subset of WIT), Infoseek, E-VQA, OKVQA and MSMARCO.
| Model | Vision Encoder | Text Encoder | Checkpoint Name | No. Param. | WIT | LLaVA | OVEN | KVQA | IGLUE | Infoseek | E-VQA | OKVQA | MSMARCO |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PreFLMR | ViT-B | Base-v2 | LinWeizheDragon/PreFLMR_ViT-B | 327M | 41.7 | 67.2 | 46.3 | 28.6 | 57.3 | 48.8 | 67.9 | 66.1 | 79.5 |
| PreFLMR | ViT-L | Base-v2 | LinWeizheDragon/PreFLMR_ViT-L | 543M | 60.5 | 71.8 | 59.8 | 43.6 | 69.2 | 57.9 | 70.8 | 68.5 | 78.7 |
| PreFLMR | ViT-G | Base-v2 | LinWeizheDragon/PreFLMR_ViT-G | 2.1B | 61.5 | 72.4 | 63.4 | 42.1 | 71.5 | 59.6 | 73.1 | 68.6 | 78.6 |
For the evaluation metrics, WIT uses Recall@10, IGLUE uses Recall@1, and all the rest datasets use Recall@5.
Citation
BibTeX:
@article{Lin_Mei_Chen_Byrne_2024,
title={PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers},
url={http://arxiv.org/abs/2402.08327},
number={arXiv:2402.08327},
publisher={arXiv},
author={Lin, Weizhe and Mei, Jingbiao and Chen, Jinghong and Byrne, Bill},
year={2024}}