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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
Visual Question AnsweringVQA v2 (test-dev)
Overall Accuracy72.8
664
Image ClassificationImageNet-1K--
524
Natural Language UnderstandingGLUE (dev)
SST-2 (Acc)90.9
504
Image ClassificationFood-101
Accuracy88.5
494
Image ClassificationDTD
Accuracy77.3
487
Image ClassificationFlowers102
Accuracy98.1
478
Image ClassificationStanford Cars
Accuracy70.9
477
Text-to-Image RetrievalFlickr30K
R@165.2
460
Natural Language UnderstandingGLUE
SST-290.9
452
Image-to-Text RetrievalFlickr30K 1K (test)
R@167.7
439
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Other info

Code

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