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--- |
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library_name: transformers |
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license: mit |
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language: |
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- en |
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tags: |
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- retrieval |
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- multi-modal |
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- knowledge-based visual question answering |
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- FLMR |
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- PreFLMR |
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--- |
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# PreFLMR model card |
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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. |
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## Model Details |
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### Model Description |
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- **Model type:** FLMRModelForRetrieval |
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- **Language(s) (NLP):** English |
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- **License:** MIT License |
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### Paper and resources for more detail |
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- **Blog Post for quick overview:** https://www.jinghong-chen.net/preflmr-sota-open-sourced-multi/ |
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- **Paper:** https://arxiv.org/abs/2402.08327 |
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- **Gradio Demo:** https://u60544-b8d4-53eaa55d.westx.seetacloud.com:8443/ |
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- **Repository:** https://github.com/LinWeizheDragon/FLMR |
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- **Project Page:** https://preflmr.github.io/ |
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## Uses |
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### Direct Use |
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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). |
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### Downstream Use |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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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) |
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## How to Get Started with the Model |
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For details of training, indexing, and performing retrieval, please refer to [here](https://github.com/LinWeizheDragon/FLMR). |
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## Training datasets |
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The model is pre-trained on three types of tasks with a total of nine datasets: |
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1. Image to Text retrieval: WIT, KVQA, and CC3M |
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2. Question to Text retrieval: MSMARCO |
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3. Image & Question to Text retrieval: LLaVA, OVEN, OKVQA, Infoseek and E-VQA |
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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](https://arxiv.org/abs/2402.08327). We will release the proprocessed datasets soon. |
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## Evaluation datasets |
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We evaluate our models on WIT, LLaVA, OVEN, KVQA, IGLUE (subset of WIT), Infoseek, E-VQA, OKVQA and MSMARCO. |
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| Model | Vision Encoder | Text Encoder | Checkpoint Name | No. Param. | WIT | LLaVA | OVEN | KVQA | IGLUE | Infoseek | E-VQA | OKVQA | MSMARCO | |
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|---------|----------------|--------------|-------------------------------------------------------------|-------|-------|--------|-------|-------|-------|----------|-------|--------|-------| |
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| PreFLMR | ViT-B | Base-v2 | [LinWeizheDragon/PreFLMR_ViT-B](https://huggingface.co/LinWeizheDragon/PreFLMR_ViT-B) | 327M | 41.7 | 67.2 | 46.3 | 28.6 | 57.3 | 48.8 | 67.9 | 66.1 | 79.5 | |
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| PreFLMR | ViT-L | Base-v2 | [LinWeizheDragon/PreFLMR_ViT-L](https://huggingface.co/LinWeizheDragon/PreFLMR_ViT-L) | 543M | 60.5 | 71.8 | 59.8 | 43.6 | 69.2 | 57.9 | 70.8 | 68.5 | 78.7 | |
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| PreFLMR | ViT-G | Base-v2 | [LinWeizheDragon/PreFLMR_ViT-G](https://huggingface.co/LinWeizheDragon/PreFLMR_ViT-G) | 2.1B | 61.5 | 72.4 | 63.4 | 42.1 |71.5 | 59.6 | 73.1 | 68.6 | 78.6 | |
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For the evaluation metrics, WIT uses Recall@10, IGLUE uses Recall@1, and all the rest datasets use Recall@5. |
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## Citation |
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**BibTeX:** |
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``` |
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@article{Lin_Mei_Chen_Byrne_2024, |
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title={PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers}, |
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url={http://arxiv.org/abs/2402.08327}, |
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number={arXiv:2402.08327}, |
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publisher={arXiv}, |
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author={Lin, Weizhe and Mei, Jingbiao and Chen, Jinghong and Byrne, Bill}, |
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year={2024}} |
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``` |
