Unsupervised large-scale vision-language pre-training has shown promising advances on various downstream tasks. Existing methods often model the cross-modal interaction either via the similarity of the global feature of each modality which misses sufficient information, or finer-grained interactions using cross/self-attention upon visual and textual tokens. However, cross/self-attention suffers from inferior efficiency in both training and inference. In this talk, we introduce a large-scale Fine-grained Interactive Language-Image Pre-training method to achieve finer-level alignment through a cross-modal late interaction mechanism, which uses a token-wise maximum similarity between visual and textual tokens to guide the contrastive objective. The resultant model FILIP and Wukong achieve good performance on multiple downstream vision-language tasks, while maintaining the inference efficiency of dual-stream models. The visualization on word-patch alignment further shows that FILIP can learn meaningful fine-grained features with promising localization ability. Furthermore, we release a 100 million Chinese image-text pair dataset for pre-training.