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. Unlike OpenAI CLIP that adopts a simple contrastive learning method, we devise a more advanced algorithm by adapting the latest method MoCo into the cross-modal scenario. By building a large queue-based dictionary, our BriVL can incorporate more negative samples in limited GPU resources. We further construct a large Chinese multi-source image-text dataset called RUC-CAS-WenLan for pre-training our BriVL model. Extensive experiments demonstrate that the pre-trained BriVL model outperforms both UNITER and OpenAI CLIP on various downstream tasks.

PDF Abstract

Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Image-to-Text Retrieval AIC-ICC CMCL Recall@1 20.3 # 1
Recall@5 37 # 1
Recall@10 45.6 # 1
Image Captioning AIC-ICC CMCL BLEU 66.1 # 1
METEOR 41.1 # 1
ROUGE-L 71.9 # 1
CIDEr 220.7 # 1
Text-to-Image Retrieval AIC-ICC CMCL Recall@1 14.4 # 1
Recall@5 30.4 # 1
Recall@10 39.1 # 1
Text-to-Image Retrieval RUC-CAS-WenLan CMCL Recall@1 36 # 1
Recall@5 55.4 # 1
Recall@10 62.1 # 1
Image-to-Text Retrieval RUC-CAS-WenLan CMCL Recall@1 36.1 # 1
Recall@5 55.5 # 1
Recall@10 62.2 # 1

Methods