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lmms-lab-encoder/flickr30k

Large-scale Multi-modality Models Evaluation Suite Accelerating the development of large-scale multi-modality models (LMMs) with lmms-eval 🏠 Homepage | 📚 Documentation | 🤗 Huggingface Datasets This Dataset This is a formatted version of flickr30k. It is used in our lmms-eval pipeline to allow for one-click evaluations of large multi-modality models. @article{young-etal-2014-image, title = "From image descriptions to visual denotations: New similarity… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab-encoder/flickr30k.

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Large-scale Multi-modality Models Evaluation Suite

Accelerating the development of large-scale multi-modality models (LMMs) with lmms-eval

🏠 Homepage | 📚 Documentation | 🤗 Huggingface Datasets

This Dataset

This is a formatted version of flickr30k. It is used in our lmms-eval pipeline to allow for one-click evaluations of large multi-modality models.

@article{young-etal-2014-image,
    title = "From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions",
    author = "Young, Peter  and
      Lai, Alice  and
      Hodosh, Micah  and
      Hockenmaier, Julia",
    editor = "Lin, Dekang  and
      Collins, Michael  and
      Lee, Lillian",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "2",
    year = "2014",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://aclanthology.org/Q14-1006",
    doi = "10.1162/tacl_a_00166",
    pages = "67--78",
    abstract = "We propose to use the visual denotations of linguistic expressions (i.e. the set of images they describe) to define novel denotational similarity metrics, which we show to be at least as beneficial as distributional similarities for two tasks that require semantic inference. To compute these denotational similarities, we construct a denotation graph, i.e. a subsumption hierarchy over constituents and their denotations, based on a large corpus of 30K images and 150K descriptive captions.",
}