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---
dataset_info:
- config_name: A-OKVQA
features:
- name: qry_text
dtype: string
- name: qry_img_path
dtype: string
- name: tgt_text
sequence: string
- name: tgt_img_path
sequence: string
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- config_name: CIFAR-100
features:
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dtype: string
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sequence: string
- name: tgt_img_path
sequence: string
splits:
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- config_name: CIRR
features:
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- config_name: ChartQA
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- config_name: Country211
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- config_name: DocVQA
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sequence: string
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- config_name: FashionIQ
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- config_name: HatefulMemes
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- config_name: ImageNet-1K
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- config_name: ImageNet-A
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- config_name: ImageNet-R
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- config_name: InfographicsVQA
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- config_name: MSCOCO
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- config_name: MSCOCO_i2t
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- config_name: MSCOCO_t2i
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- config_name: N24News
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- config_name: NIGHTS
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- config_name: OK-VQA
features:
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- config_name: OVEN
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splits:
- name: test
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- config_name: ObjectNet
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- config_name: Place365
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- config_name: RefCOCO
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- config_name: RefCOCO-Matching
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- config_name: SUN397
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- config_name: ScienceQA
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- config_name: TextVQA
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- config_name: VOC2007
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- config_name: VisDial
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splits:
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- config_name: VisualNews_i2t
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- config_name: VisualNews_t2i
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sequence: string
splits:
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- config_name: VizWiz
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- config_name: WebQA
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- config_name: Wiki-SS-NQ
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splits:
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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 [email protected].
## 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}
}
```