Scaling FLAVA Overview
FLAVA is a foundation multimodal model which consists of transformer based image and text encoders followed by a transformer-based multimodal fusion module. It is pretrained on both unimodal and multimodal data with a diverse set of losses. This includes masked language, image and multimodal modeling losses that require the model to reconstruct the original input from its context (self-supervised learning). It also uses image text matching loss over positive and negative examples of aligned image-text pairs as well as CLIP style contrastive loss. In addition to multimodal tasks (like image-text retrieval), FLAVA demonstrated competitive performance on unimodal benchmarks as well (GLUE tasks for NLP and image classification for vision).


