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FLAVA: A Foundational Language And Vision Alignment Model

About

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.

Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, Douwe Kiela• 2021

Related benchmarks

TaskDatasetResultRank
Image ClassificationImageNet 1k (test)
Top-1 Accuracy61.41
939
Visual Question AnsweringVQA v2 (test-dev)
Overall Accuracy72.8
721
Image ClassificationStanford Cars
Accuracy70.9
705
Image ClassificationDTD
Accuracy77.3
610
Text-to-Image RetrievalFlickr30K
R@165.2
607
Image ClassificationImageNet-1K--
600
Image ClassificationFood-101
Accuracy88.5
590
Image ClassificationFlowers102
Accuracy98.1
558
Natural Language UnderstandingGLUE
SST-290.9
551
Natural Language UnderstandingGLUE (dev)
SST-2 (Acc)90.9
529
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Code

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