  In this work, we pursue a unified paradigm for multimodal pretraining to
break the scaffolds of complex task/modality-specific customization. We propose
OFA, a Task-Agnostic and Modality-Agnostic framework that supports Task
Comprehensiveness. OFA unifies a diverse set of cross-modal and unimodal tasks,
including image generation, visual grounding, image captioning, image
classification, language modeling, etc., in a simple sequence-to-sequence
learning framework. OFA follows the instruction-based learning in both
pretraining and finetuning stages, requiring no extra task-specific layers for
downstream tasks. In comparison with the recent state-of-the-art vision &
language models that rely on extremely large cross-modal datasets, OFA is
pretrained on only 20M publicly available image-text pairs. Despite its
simplicity and relatively small-scale training data, OFA achieves new SOTAs in
a series of cross-modal tasks while attaining highly competitive performances
on uni-modal tasks. Our further analysis indicates that OFA can also
effectively transfer to unseen tasks and unseen domains. Our code and models
are publicly available at https://github.com/OFA-Sys/OFA.
