Papers › FLAVA: A Foundational Language And Vision Alignment Model
FLAVA: A Foundational Language And Vision Alignment Model
Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, Douwe Kiela
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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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Retrieval | COCO (Common Objects in Context) | FLAVA (zero-shot) | recall@1 | 38.38 | #4 of 6 | Archive leaderboard | report |
| Image Retrieval | COCO (Common Objects in Context) | FLAVA (zero-shot) | recall@5 | 67.47 | #4 of 6 | Archive leaderboard | report |
| Image Retrieval | COCO (Common Objects in Context) | CLIP (zero-shot) | recall@1 | 33.29 | #5 of 6 | Archive leaderboard | report |
| Image Retrieval | COCO (Common Objects in Context) | CLIP (zero-shot) | recall@5 | 62.47 | #5 of 6 | Archive leaderboard | report |
| Image-to-Text Retrieval | COCO (Common Objects in Context) | FLAVA (ViT-B, zero-shot) | Recall@1 | 42.74 | #6 of 9 | Archive leaderboard | report |
| Image-to-Text Retrieval | COCO (Common Objects in Context) | FLAVA (ViT-B, zero-shot) | Recall@5 | 76.76 | #6 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: FLAVA
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