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FLAVA: A Foundational Language And Vision Alignment Model

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arxiv 2112.04482 v3 pith:GVXXTUZZ submitted 2021-12-08 cs.CV cs.CL

FLAVA: A Foundational Language And Vision Alignment Model

Amanpreet Singh , Ronghang Hu , Vedanuj Goswami , Guillaume Couairon , Wojciech Galuba , Marcus Rohrbach , Douwe Kiela This is my paper
classification cs.CV cs.CL
keywords tasksvisionlanguagemodelmodalitiesflavafoundationgood
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic pretraining for obtaining good performance on a variety of downstream tasks. Generally, such models are often either cross-modal (contrastive) or multi-modal (with earlier fusion) but not both; and they often only target specific modalities or tasks. A promising direction would be to use a single holistic universal model, as a "foundation", that targets all modalities at once -- a true vision and language foundation model should be good at vision tasks, language tasks, and cross- and multi-modal vision and language tasks. We introduce FLAVA as such a model and demonstrate impressive performance on a wide range of 35 tasks spanning these target modalities.

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Cited by 3 Pith papers

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    CoCa unifies contrastive and generative pretraining in one image-text model to reach 86.3% zero-shot ImageNet accuracy and new state-of-the-art results on multiple downstream benchmarks.