Overview of OFA
OFA is a Task-Agnostic and Modality-Agnostic framework that supports Task Comprehensiveness. This framework is used for multimodal pretraining in a simple sequence-to-sequence learning framework. OFA is interested in unifying a diverse set of cross-modal and unimodal tasks, including image generation, visual grounding, image captioning, image classification, language modeling, and many other tasks.
Unified paradigm for multimodal pretraining
OFA assists in breaking the scaffolds of complex task/modality-specific customization. It follows the instruction-based learning in both pretraining and finetuning stages, requiring no extra task-specific layers for downstream tasks. This makes the framework useful and user-friendly.
Simple sequence-to-sequence learning framework
A sequence-to-sequence learning framework follows a sequence of input words or tokens and attempts to increase accuracy by predicting the next appropriate word or token. This framework is not only simple and easy to use but targets a wide range of cross-modal and unimodal tasks. OFA uses this sequence-to-sequence learning framework to enhance its accuracy and ease for users.
Unifying cross-modal and unimodal tasks
OFA is designed to unify cross-modal and unimodal tasks. Its purpose is to reduce complexity by bringing together various tasks under one umbrella with no need for different models. OFA achieves new state-of-the-art results in a series of cross-modal tasks while still being highly competitive in uni-modal tasks.
Pretraining on 20M publicly available image-text pairs
In comparison with recent state-of-the-art vision & language models that rely on extremely large cross-modal datasets, OFA is pre-trained on only 20M publicly available image-text pairs. This is a great advantage for users, as the models they train can still produce new SOTAs in a series of cross-modal and unimodal tasks.
Effective transfer to unseen tasks and domains
Further analysis indicates that OFA can effectively transfer to unseen tasks and unseen domains. This is a valuable quality as tasks and applications change and evolve over time. Users can modify and update their OFA models without retraining them.
In summary
OFA is a unifying framework, designed to bring together multiple tasks under one umbrella that is easy for users, efficient and competitive. It can pretrain and finetune models for a range of tasks, and remains competitive with state-of-the-art models despite being trained on a relatively small dataset. OFA has many benefits for users, including ease of use, modular design, and the ability to transfer models to new tasks and domains.
