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metadata
dataset_info:
  - config_name: A-OKVQA
    features:
      - name: qry_text
        dtype: string
      - name: qry_img_path
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      - name: tgt_text
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  - config_name: CIFAR-100
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  - config_name: CIRR
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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: EDIS
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        sequence: string
      - name: tgt_img_path
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  - config_name: FashionIQ
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  - config_name: GQA
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      - name: test
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  - config_name: HatefulMemes
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      - name: qry_img_path
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      - name: tgt_text
        sequence: string
      - name: tgt_img_path
        sequence: string
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      - name: test
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  - config_name: ImageNet-1K
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      - name: test
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  - config_name: ImageNet-A
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  - config_name: ImageNet-R
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      - name: qry_img_path
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      - name: test
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  - config_name: InfographicsVQA
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      - name: qry_img_path
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      - name: tgt_img_path
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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
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      - name: qry_img_path
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  - config_name: OVEN
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      - name: test
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  - config_name: ObjectNet
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      - name: qry_img_path
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      - name: tgt_img_path
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    splits:
      - name: test
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        num_examples: 1000
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  - config_name: Place365
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      - name: test
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        num_examples: 1000
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  - config_name: RefCOCO
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  - config_name: RefCOCO-Matching
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      - name: test
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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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      - name: test
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  - config_name: VisDial
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  - config_name: Visual7W
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      - name: qry_img_path
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    splits:
      - name: test
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  - config_name: Visual7W-Pointing
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    splits:
      - name: test
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  - config_name: VisualNews_i2t
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      - name: qry_img_path
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  - config_name: VisualNews_t2i
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      - name: qry_img_path
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  - config_name: VizWiz
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      - name: qry_text
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      - name: qry_img_path
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    splits:
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  - config_name: WebQA
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      - name: qry_img_path
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    splits:
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  - config_name: Wiki-SS-NQ
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      - name: qry_text
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      - name: qry_img_path
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        sequence: string
    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.

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: abs

Per-dataset Results

We list the performance of different embedding models in the following: abs

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}
}