File size: 3,849 Bytes
2668634
 
f4edb9c
 
 
67d9380
 
 
 
 
 
2668634
 
f4edb9c
2668634
3dd99ee
 
 
2668634
 
 
3dd99ee
f4edb9c
 
2668634
f4edb9c
2668634
f4edb9c
 
 
 
 
2668634
 
 
 
 
3dd99ee
2668634
f4edb9c
2668634
 
 
f4edb9c
2668634
 
 
67d9380
2668634
f4edb9c
67d9380
 
f4edb9c
 
2668634
f4edb9c
2668634
 
f4edb9c
 
 
 
 
 
 
2668634
f4edb9c
2668634
 
f4edb9c
2668634
 
f4edb9c
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
---
library_name: transformers
license: mit
language:
- en
tags:
- retrieval
- multi-modal
- knowledge-based visual question answering
- FLMR
- PreFLMR
---

# 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](https://github.com/LinWeizheDragon/FLMR).

### Downstream Use 

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

This model can be used combined with language models to create a retrieval-augmented language model. The useage 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 three types of tasks with a total of nine datasets:
1. Image to Text retrieval: WIT, KVQA, and CC3M
2. Question to Text retrieval: MSMARCO
3. 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](https://arxiv.org/abs/2402.08327). 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](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 |
| 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 |
| 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 |

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}}
```