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  1. A-OKVQA/test-00000-of-00001.parquet +3 -0
  2. CIFAR-100/test-00000-of-00001.parquet +3 -0
  3. CIRR/test-00000-of-00001.parquet +3 -0
  4. ChartQA/test-00000-of-00001.parquet +3 -0
  5. Country211/test-00000-of-00001.parquet +3 -0
  6. DocVQA/test-00000-of-00001.parquet +3 -0
  7. EDIS/test-00000-of-00001.parquet +3 -0
  8. FashionIQ/test-00000-of-00001.parquet +3 -0
  9. GQA/test-00000-of-00001.parquet +3 -0
  10. HatefulMemes/test-00000-of-00001.parquet +3 -0
  11. ImageNet-1K/test-00000-of-00001.parquet +3 -0
  12. ImageNet-A/test-00000-of-00001.parquet +3 -0
  13. ImageNet-R/test-00000-of-00001.parquet +3 -0
  14. InfographicsVQA/test-00000-of-00001.parquet +3 -0
  15. MSCOCO/test-00000-of-00001.parquet +3 -0
  16. MSCOCO_i2t/test-00000-of-00001.parquet +3 -0
  17. MSCOCO_t2i/test-00000-of-00001.parquet +3 -0
  18. N24News/test-00000-of-00001.parquet +3 -0
  19. NIGHTS/test-00000-of-00001.parquet +3 -0
  20. OK-VQA/test-00000-of-00001.parquet +3 -0
  21. OVEN/test-00000-of-00001.parquet +3 -0
  22. ObjectNet/test-00000-of-00001.parquet +3 -0
  23. Place365/test-00000-of-00001.parquet +3 -0
  24. README.md +930 -3
  25. RefCOCO-Matching/test-00000-of-00001.parquet +3 -0
  26. RefCOCO/test-00000-of-00001.parquet +3 -0
  27. SUN397/test-00000-of-00001.parquet +3 -0
  28. ScienceQA/test-00000-of-00001.parquet +3 -0
  29. TextVQA/test-00000-of-00001.parquet +3 -0
  30. VOC2007/test-00000-of-00001.parquet +3 -0
  31. VisDial/test-00000-of-00001.parquet +3 -0
  32. Visual7W-Pointing/test-00000-of-00001.parquet +3 -0
  33. Visual7W/test-00000-of-00001.parquet +3 -0
  34. VisualNews_i2t/test-00000-of-00001.parquet +3 -0
  35. VisualNews_t2i/test-00000-of-00001.parquet +3 -0
  36. VizWiz/test-00000-of-00001.parquet +3 -0
  37. WebQA/test-00000-of-00001.parquet +3 -0
  38. Wiki-SS-NQ/test-00000-of-00001.parquet +3 -0
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+ path: CIFAR-100/test-*
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+ - split: test
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+ path: ObjectNet/test-*
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+ - config_name: Place365
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+ path: Place365/test-*
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+ - config_name: RefCOCO
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+ - split: test
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+ path: RefCOCO/test-*
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+ - config_name: RefCOCO-Matching
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+ data_files:
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+ - split: test
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+ path: RefCOCO-Matching/test-*
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+ - config_name: SUN397
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+ data_files:
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+ - split: test
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+ path: SUN397/test-*
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+ - config_name: ScienceQA
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+ data_files:
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+ - split: test
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+ path: ScienceQA/test-*
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+ - config_name: TextVQA
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+ data_files:
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+ - split: test
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+ path: TextVQA/test-*
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+ - config_name: VOC2007
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+ data_files:
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+ - split: test
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+ path: VOC2007/test-*
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+ - config_name: VisDial
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+ data_files:
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+ - split: test
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+ path: VisDial/test-*
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+ - config_name: Visual7W
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+ data_files:
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+ - split: test
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+ path: Visual7W/test-*
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+ - config_name: Visual7W-Pointing
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+ data_files:
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+ - split: test
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+ path: Visual7W-Pointing/test-*
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+ - config_name: VisualNews_i2t
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+ data_files:
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+ - split: test
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+ path: VisualNews_i2t/test-*
876
+ - config_name: VisualNews_t2i
877
+ data_files:
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+ - split: test
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+ path: VisualNews_t2i/test-*
880
+ - config_name: VizWiz
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+ data_files:
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+ - split: test
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+ path: VizWiz/test-*
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+ - config_name: WebQA
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+ data_files:
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+ - split: test
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+ path: WebQA/test-*
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+ - config_name: Wiki-SS-NQ
889
+ data_files:
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+ - split: test
891
+ path: Wiki-SS-NQ/test-*
892
+ license: apache-2.0
893
+ language:
894
+ - en
895
+ tags:
896
+ - ranking
897
+ pretty_name: MMEB
898
+ size_categories:
899
+ - 10K<n<100K
900
+ ---
901
+
902
+ # Massive Multimodal Embedding Benchmark
903
+
904
+ 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.
905
+
906
+ The dataset is published in our paper [VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks](https://arxiv.org/abs/2410.05160).
907
+
908
+ ## Dataset Usage
909
+ 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.
910
+
911
+ ## Statistics
912
+ We show the statistics of all the datasets as follows:
913
+ <img width="900" alt="abs" src="statistics.png">
914
+
915
+ ## Per-dataset Results
916
+ We list the performance of different embedding models in the following:
917
+ <img width="900" alt="abs" src="leaderboard.png">
918
+
919
+ ## Submission
920
+ 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].
921
+
922
+ ## Cite Us
923
+ ```
924
+ @article{jiang2024vlm2vec,
925
+ title={VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks},
926
+ author={Jiang, Ziyan and Meng, Rui and Yang, Xinyi and Yavuz, Semih and Zhou, Yingbo and Chen, Wenhu},
927
+ journal={arXiv preprint arXiv:2410.05160},
928
+ year={2024}
929
+ }
930
+ ```
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