|
--- |
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dataset_info: |
|
- config_name: A-OKVQA |
|
features: |
|
- name: qry_text |
|
dtype: string |
|
- name: qry_img_path |
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splits: |
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- name: test |
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num_examples: 1000 |
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download_size: 1168340 |
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dataset_size: 14048199 |
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- config_name: CIFAR-100 |
|
features: |
|
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splits: |
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- name: test |
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num_bytes: 1519890 |
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num_examples: 1000 |
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download_size: 20544 |
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dataset_size: 1519890 |
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- config_name: CIRR |
|
features: |
|
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dtype: string |
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num_examples: 1000 |
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download_size: 1565489 |
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dataset_size: 70162098 |
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- config_name: ChartQA |
|
features: |
|
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dtype: string |
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num_examples: 1000 |
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download_size: 1434448 |
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- config_name: Country211 |
|
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|
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num_examples: 1000 |
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download_size: 31556 |
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dataset_size: 3678000 |
|
- config_name: DocVQA |
|
features: |
|
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dtype: string |
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num_examples: 1000 |
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download_size: 1734476 |
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- config_name: EDIS |
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dtype: string |
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splits: |
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num_examples: 1000 |
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download_size: 3350382 |
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dataset_size: 184208708 |
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- config_name: FashionIQ |
|
features: |
|
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dtype: string |
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splits: |
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num_examples: 1000 |
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download_size: 1729457 |
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- config_name: GQA |
|
features: |
|
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dtype: string |
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num_examples: 1000 |
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download_size: 1764457 |
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dataset_size: 40809641 |
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- config_name: HatefulMemes |
|
features: |
|
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dtype: string |
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splits: |
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- name: test |
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num_examples: 1000 |
|
download_size: 9972 |
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- config_name: ImageNet-1K |
|
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|
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splits: |
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num_examples: 1000 |
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download_size: 185019 |
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- config_name: ImageNet-A |
|
features: |
|
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splits: |
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num_examples: 1000 |
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download_size: 147780 |
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- config_name: ImageNet-R |
|
features: |
|
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splits: |
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num_examples: 1000 |
|
download_size: 23656 |
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dataset_size: 3456890 |
|
- config_name: InfographicsVQA |
|
features: |
|
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|
dtype: string |
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- name: qry_img_path |
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splits: |
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- name: test |
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num_bytes: 19114439 |
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num_examples: 1000 |
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download_size: 1439837 |
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dataset_size: 19114439 |
|
- config_name: MSCOCO |
|
features: |
|
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dtype: string |
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- name: qry_img_path |
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splits: |
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num_examples: 1000 |
|
download_size: 1681753 |
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dataset_size: 97759085 |
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- config_name: MSCOCO_i2t |
|
features: |
|
- name: qry_text |
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dtype: string |
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- name: qry_img_path |
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splits: |
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num_examples: 1000 |
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download_size: 1785583 |
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- config_name: MSCOCO_t2i |
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features: |
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dtype: string |
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splits: |
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num_examples: 1000 |
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- config_name: N24News |
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features: |
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- config_name: NIGHTS |
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features: |
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num_examples: 1000 |
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- config_name: OK-VQA |
|
features: |
|
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splits: |
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num_examples: 1000 |
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- config_name: OVEN |
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num_examples: 1000 |
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- config_name: ObjectNet |
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|
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splits: |
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- name: test |
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num_examples: 1000 |
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download_size: 27132 |
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dataset_size: 2036000 |
|
- config_name: Place365 |
|
features: |
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splits: |
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num_examples: 1000 |
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download_size: 89866 |
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- config_name: RefCOCO |
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features: |
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num_examples: 1000 |
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download_size: 1858145 |
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- config_name: RefCOCO-Matching |
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num_examples: 1000 |
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- config_name: SUN397 |
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num_examples: 1000 |
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download_size: 118447 |
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|
- config_name: ScienceQA |
|
features: |
|
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dtype: string |
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num_examples: 1000 |
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- config_name: TextVQA |
|
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|
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dtype: string |
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- config_name: VOC2007 |
|
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|
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splits: |
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num_examples: 1000 |
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download_size: 13813 |
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dataset_size: 368000 |
|
- config_name: VisDial |
|
features: |
|
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|
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- name: qry_img_path |
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splits: |
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num_examples: 1000 |
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download_size: 1730820 |
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- config_name: Visual7W |
|
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|
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- name: qry_img_path |
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splits: |
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num_examples: 1000 |
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download_size: 1564788 |
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- config_name: Visual7W-Pointing |
|
features: |
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num_examples: 1000 |
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download_size: 1299380 |
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- config_name: VisualNews_i2t |
|
features: |
|
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dtype: string |
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- name: qry_img_path |
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sequence: string |
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splits: |
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num_examples: 1000 |
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download_size: 81491360 |
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|
- config_name: VisualNews_t2i |
|
features: |
|
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sequence: string |
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sequence: string |
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splits: |
|
- name: test |
|
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num_examples: 1000 |
|
download_size: 1763677 |
|
dataset_size: 97176206 |
|
- config_name: VizWiz |
|
features: |
|
- name: qry_text |
|
dtype: string |
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- name: qry_img_path |
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sequence: string |
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sequence: string |
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splits: |
|
- name: test |
|
num_bytes: 20550246 |
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num_examples: 1000 |
|
download_size: 1425789 |
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dataset_size: 20550246 |
|
- config_name: WebQA |
|
features: |
|
- name: qry_text |
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dtype: string |
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num_examples: 1000 |
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download_size: 3257136 |
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|
- config_name: Wiki-SS-NQ |
|
features: |
|
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- name: test |
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num_examples: 1000 |
|
download_size: 1900579 |
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dataset_size: 74583207 |
|
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-* |
|
--- |
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|
Embedding models have been crucial in enabling various downstream tasks such as semantic similarity, information retrieval, and clustering. Recently, there has been a surge of interest in developing universal text embedding models that can generalize across tasks (e.g., MTEB). However, progress in learning universal multimodal embedding models has been relatively slow despite their importance. In this work, we aim to explore the potential for building universal embeddings capable of handling a wide range of downstream tasks. Our contributions are twofold: (1) MMEB (Massive Multimodal Embedding Benchmark), which covers 4 meta-tasks including classification, question answering, retrieval, and visual grounding and 36 datasets, including 20 training and 16 evaluation datasets, and (2) VLM2Vec (Vision-Language Model => Vector), a contrastive training framework that converts any state-of-the-art vision-language model into an embedding model via training on MMEB. Unlike previous models such as CLIP and BLIP, VLM2Vec can process any combination of images and text to generate a fixed-dimensional vector based on task instructions. We build a series of VLM2Vec models on Phi-3.5-V and evaluate them on MMEB's evaluation split. Our results show that VLM2Vec achieves an absolute average improvement of 10% to 20% over existing multimodal embedding models on both in-distribution and out-of-distribution datasets in MMEB. |
