FLAVA: A Foundational Language And Vision Alignment Model

作者：Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, Douwe Kiela · 发表于：2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 年份：2022 · DOI：10.1109/cvpr52688.2022.01519 · 被引用次数：508 · 研究领域：Multimodal Machine Learning Applications、Advanced Image and Video Retrieval Techniques

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.