README.md

---
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- config_name: HatefulMemes
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- config_name: ImageNet-1K
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- config_name: InfographicsVQA
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- config_name: NIGHTS
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- config_name: OK-VQA
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- config_name: TextVQA
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- config_name: VOC2007
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- config_name: WebQA
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- config_name: Wiki-SS-NQ
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configs:
- config_name: A-OKVQA
  data_files:
  - split: test
    path: A-OKVQA/test-*
- config_name: CIFAR-100
  data_files:
  - split: test
    path: CIFAR-100/test-*
- config_name: CIRR
  data_files:
  - split: test
    path: CIRR/test-*
- config_name: ChartQA
  data_files:
  - split: test
    path: ChartQA/test-*
- config_name: Country211
  data_files:
  - split: test
    path: Country211/test-*
- config_name: DocVQA
  data_files:
  - split: test
    path: DocVQA/test-*
- config_name: EDIS
  data_files:
  - split: test
    path: EDIS/test-*
- config_name: FashionIQ
  data_files:
  - split: test
    path: FashionIQ/test-*
- config_name: GQA
  data_files:
  - split: test
    path: GQA/test-*
- config_name: HatefulMemes
  data_files:
  - split: test
    path: HatefulMemes/test-*
- config_name: ImageNet-1K
  data_files:
  - split: test
    path: ImageNet-1K/test-*
- config_name: ImageNet-A
  data_files:
  - split: test
    path: ImageNet-A/test-*
- config_name: ImageNet-R
  data_files:
  - split: test
    path: ImageNet-R/test-*
- config_name: InfographicsVQA
  data_files:
  - split: test
    path: InfographicsVQA/test-*
- config_name: MSCOCO
  data_files:
  - split: test
    path: MSCOCO/test-*
- config_name: MSCOCO_i2t
  data_files:
  - split: test
    path: MSCOCO_i2t/test-*
- config_name: MSCOCO_t2i
  data_files:
  - split: test
    path: MSCOCO_t2i/test-*
- config_name: N24News
  data_files:
  - split: test
    path: N24News/test-*
- config_name: NIGHTS
  data_files:
  - split: test
    path: NIGHTS/test-*
- config_name: OK-VQA
  data_files:
  - split: test
    path: OK-VQA/test-*
- config_name: OVEN
  data_files:
  - split: test
    path: OVEN/test-*
- config_name: ObjectNet
  data_files:
  - split: test
    path: ObjectNet/test-*
- config_name: Place365
  data_files:
  - split: test
    path: Place365/test-*
- config_name: RefCOCO
  data_files:
  - split: test
    path: RefCOCO/test-*
- config_name: RefCOCO-Matching
  data_files:
  - split: test
    path: RefCOCO-Matching/test-*
- config_name: SUN397
  data_files:
  - split: test
    path: SUN397/test-*
- config_name: ScienceQA
  data_files:
  - split: test
    path: ScienceQA/test-*
- config_name: TextVQA
  data_files:
  - split: test
    path: TextVQA/test-*
- config_name: VOC2007
  data_files:
  - split: test
    path: VOC2007/test-*
- config_name: VisDial
  data_files:
  - split: test
    path: VisDial/test-*
- config_name: Visual7W
  data_files:
  - split: test
    path: Visual7W/test-*
- config_name: Visual7W-Pointing
  data_files:
  - split: test
    path: Visual7W-Pointing/test-*
- config_name: VisualNews_i2t
  data_files:
  - split: test
    path: VisualNews_i2t/test-*
- config_name: VisualNews_t2i
  data_files:
  - split: test
    path: VisualNews_t2i/test-*
- config_name: VizWiz
  data_files:
  - split: test
    path: VizWiz/test-*
- config_name: WebQA
  data_files:
  - split: test
    path: WebQA/test-*
- config_name: Wiki-SS-NQ
  data_files:
  - split: test
    path: Wiki-SS-NQ/test-*
license: apache-2.0
language:
- en
tags:
- ranking
pretty_name: MMEB
size_categories:
- 10K<n<100K
---

# Massive Multimodal Embedding Benchmark

We compile a large set of evaluation tasks to understand the capabilities of multimodal embedding models. This benchmark covers 4 meta tasks and 36 datasets meticulously selected for evaluation.

The dataset is published in our paper [VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks](https://arxiv.org/abs/2410.05160).

## Dataset Usage
For each dataset, we have 1000 examples for evaluation. Each example contains a query and a set of targets. Both the query and target could be any combination of image and text. The first one in the candidate list is the groundtruth target.  

## Statistics
We show the statistics of all the datasets as follows:
<img width="900" alt="abs" src="statistics.png">

## Per-dataset Results
We list the performance of different embedding models in the following:
<img width="900" alt="abs" src="leaderboard.png">

## Submission
We will set a formal leaderboard soon. If you want to add your results to the leaderboard, please send email to us at ziyanjiang528@gmail.com.

## Cite Us
```
@article{jiang2024vlm2vec,
  title={VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks},
  author={Jiang, Ziyan and Meng, Rui and Yang, Xinyi and Yavuz, Semih and Zhou, Yingbo and Chen, Wenhu},
  journal={arXiv preprint arXiv:2410.05160},
  year={2024}
}
```