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FLAVA: A Foundational Language And Vision Alignment Model
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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.
Forward citations
Cited by 2 Pith papers
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Learning to Detect Cross-Modal Negation: An Analysis of Latent Representations and an Attention-Based Solution
Negation is reported as a non-separable signal in standard VLM embeddings, cross-modal attention recovers up to +7% F1, and the paper claims visual negation depends on textual context.
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