Prefix Language Models are Unified Modal Learners

With the success of vision-language pre-training, we have witnessed the state-of-the-art has been pushed on multi-modal understanding and generation. However, the current pre-training paradigm is either incapable of targeting all modalities at once (e.g., text generation and image generation), or requires multi-fold well-designed tasks which significantly limits the scalability. We demonstrate that a unified modal model could be learned with a prefix language modeling objective upon text and image sequences. Thanks to the simple but powerful pre-training paradigm, our proposed model, DaVinci, is simple to train, scalable to huge data, and adaptable to a variety of downstream tasks across modalities (language / vision / vision+language), types (understanding / generation) and settings (e.g., zero-shot, fine-tuning, linear evaluation) with a single unified architecture. DaVinci achieves the competitive performance on a wide range of 26 understanding / generation tasks, and outperforms previous unified vision-language models on most tasks, including ImageNet classification (+1.6%), VQAv2 (+1.4%), COCO caption generation (BLEU@4 +1.1%, CIDEr +1.5%) and COCO image generation (IS +0.9%, FID -1.0%), at the comparable model and data scale. Furthermore, we offer a well-defined benchmark for future research by reporting the performance on different scales of the pre-training dataset on a heterogeneous and wide distribution coverage. Our results establish new, stronger baselines for future comparisons at different data scales and shed light on the difficulties of comparing VLP models more generally.