2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2022 · 494 citations · 83 references
ComputingComputer ScienceVision ScienceComputer VisionNatural Language ProcessingVisual PerceptionDeep LearningLanguage ModelingMulti-task LearningMultimodal InteractionCross-modal AlignmentCross-modal Feature AlignmentCross-modal FusionImage-text AlignmentLanguage Processing ModelsMultimodal Feature FusionMultimodal FusionNeural Language Models
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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