WenLan: Efficient Large-Scale Multi-Modal Pre-Training on Real World Data

Multi-modal pre-training models have been intensively explored to bridge vision and language in recent years. However, most of them explicitly model the cross-modal interaction between image-text pairs, by assuming that there exists strong semantic correlation between the text and image modalities. Since this strong assumption is often invalid in real-world scenarios, we choose to implicitly model the cross-modal correlation for large-scale multi-modal pre-training, which is the focus of the Chinese project 'WenLan' led by our team. Specifically, with the weak correlation assumption over image-text pairs, we propose a two-tower pre-training model called BriVL within the cross-modal contrastive learning framework [1]. We construct a large Chinese multi-source dataset of 650 million image-text pairs for pre-training our model. Extensive experiments demonstrate that WenLan on various downstream tasks and easy to build efficient applications based on searching between images and texts.