LinWeizheDragon
commited on
Commit
•
05c02b4
1
Parent(s):
23579dd
Create README.md
Browse files
README.md
ADDED
@@ -0,0 +1,78 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
library_name: transformers
|
3 |
+
license: mit
|
4 |
+
language:
|
5 |
+
- en
|
6 |
+
tags:
|
7 |
+
- retrieval
|
8 |
+
- multi-modal
|
9 |
+
- knowledge-based visual question answering
|
10 |
+
- FLMR
|
11 |
+
- PreFLMR
|
12 |
+
---
|
13 |
+
|
14 |
+
# FLMR model card
|
15 |
+
|
16 |
+
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.
|
17 |
+
|
18 |
+
## Model Details
|
19 |
+
|
20 |
+
### Model Description
|
21 |
+
|
22 |
+
- **Model type:** FLMRModelForRetrieval
|
23 |
+
- **Language(s) (NLP):** English
|
24 |
+
- **License:** MIT License
|
25 |
+
|
26 |
+
### Paper and resources for more detail
|
27 |
+
|
28 |
+
- **Blog Post for quick overview:** https://www.jinghong-chen.net/fined-grained-late-interaction-multimodal-retrieval-flmr/
|
29 |
+
- **Paper:** https://openreview.net/forum?id=IWWWulAX7g
|
30 |
+
- **Repository:** https://github.com/LinWeizheDragon/FLMR
|
31 |
+
|
32 |
+
## Uses
|
33 |
+
|
34 |
+
### Direct Use
|
35 |
+
|
36 |
+
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).
|
37 |
+
|
38 |
+
### Downstream Use
|
39 |
+
|
40 |
+
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
|
41 |
+
|
42 |
+
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)
|
43 |
+
|
44 |
+
## How to Get Started with the Model
|
45 |
+
|
46 |
+
For details of training, indexing, and performing retrieval, please refer to [here](https://github.com/LinWeizheDragon/FLMR).
|
47 |
+
|
48 |
+
## Training datasets
|
49 |
+
The model is pre-trained on
|
50 |
+
1. Image to Text retrieval: WIT
|
51 |
+
3. Image & Question to Text retrieval: OKVQA
|
52 |
+
|
53 |
+
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).
|
54 |
+
|
55 |
+
The processed datasets are:
|
56 |
+
- https://huggingface.co/datasets/BByrneLab/OKVQA_FLMR_preprocessed_data
|
57 |
+
- https://huggingface.co/datasets/BByrneLab/OKVQA_FLMR_preprocessed_GoogleSearch_passages
|
58 |
+
|
59 |
+
|
60 |
+
## Evaluation datasets
|
61 |
+
|
62 |
+
The model is evaluated on OKVQA, Infoseek, and FVQA.
|
63 |
+
|
64 |
+
Please find the evaluation results in [the paper](https://openreview.net/forum?id=IWWWulAX7g).
|
65 |
+
|
66 |
+
## Citation
|
67 |
+
|
68 |
+
**BibTeX:**
|
69 |
+
```
|
70 |
+
@inproceedings{
|
71 |
+
lin2023finegrained,
|
72 |
+
title={Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering},
|
73 |
+
author={Weizhe Lin and Jinghong Chen and Jingbiao Mei and Alexandru Coca and Bill Byrne},
|
74 |
+
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
|
75 |
+
year={2023},
|
76 |
+
url={https://openreview.net/forum?id=IWWWulAX7g}
|
77 |
+
}
|
78 |
+
```
|