FLAVA: A Foundational Language And Vision Alignment Model

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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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Image Retrieval COCO FLAVA (zero-shot) recall@1 38.38 # 4
recall@5 67.47 # 4
Image-to-Text Retrieval COCO FLAVA (zero-shot) Recall@1 42.74 # 5
Recall@5 76.76 # 5
Image-to-Text Retrieval COCO CLIP (zero-shot) Recall@1 37.12 # 6
Recall@5 69.48 # 6
Image Retrieval COCO CLIP (zero-shot) recall@1 33.29 # 5
recall@5 62.47 # 5