M2-Encoder: Advancing Bilingual Image-Text Understanding by Large-scale Efficient Pretraining

29 Jan 2024  ·  Qingpei Guo, Furong Xu, Hanxiao Zhang, Wang Ren, Ziping Ma, Lin Ju, Jian Wang, Jingdong Chen, Ming Yang ·

Vision-language foundation models like CLIP have revolutionized the field of artificial intelligence. Nevertheless, VLM models supporting multi-language, e.g., in both Chinese and English, have lagged due to the relative scarcity of large-scale pretraining datasets. Toward this end, we introduce a comprehensive bilingual (Chinese-English) dataset BM-6B with over 6 billion image-text pairs, aimed at enhancing multimodal foundation models to well understand images in both languages. To handle such a scale of dataset, we propose a novel grouped aggregation approach for image-text contrastive loss computation, which reduces the communication overhead and GPU memory demands significantly, facilitating a 60% increase in training speed. We pretrain a series of bilingual image-text foundation models with an enhanced fine-grained understanding ability on BM-6B, the resulting models, dubbed as $M^2$-Encoders (pronounced "M-Square"), set new benchmarks in both languages for multimodal retrieval and classification tasks. Notably, Our largest $M^2$-Encoder-10B model has achieved top-1 accuracies of 88.5% on ImageNet and 80.7% on ImageNet-CN under a zero-shot classification setting, surpassing previously reported SoTA methods by 2.2% and 21.1%, respectively. The $M^2$-Encoder series represents one of the most comprehensive bilingual image-text foundation models to date, so we are making it available to the research community for further exploration and development.

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Results from the Paper


 Ranked #1 on Zero-shot Image Retrieval on Flickr30k-CN (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Benchmark
Zero-Shot Cross-Modal Retrieval COCO 2014 M2-Encoder Image-to-text R@1 72.8 # 2
Image-to-text R@5 92.3 # 1
Image-to-text R@10 96.3 # 1
Text-to-image R@1 56.5 # 2
Text-to-image R@5 81.6 # 1
Text-to-image R@10 88.8 # 1
Zero-shot Text-to-Image Retrieval COCO-CN M2-Encoder Recall@1 78.7 # 1
Recall@5 96.0 # 1
Recall@10 98.7 # 1
Zero-shot Image Retrieval COCO-CN M2-Encoder R@1 78.7 # 1
R@5 96.0 # 1
R@10 98.7 # 1
Zero-Shot Cross-Modal Retrieval Flickr30k M2-Encoder Image-to-text R@1 91.2 # 6
Image-to-text R@5 99.2 # 7
Image-to-text R@10 99.6 # 11
Text-to-image R@1 92.2 # 1
Text-to-image R@5 99.5 # 1
Text-to-image R@10 99.7 # 1
Zero-shot Image Retrieval Flickr30k-CN M2-Encoder R@1 81.5 # 1
R@5 96.2 # 1
R@10 98.5 # 1
Zero-Shot Transfer Image Classification ImageNet M2-Encoder Accuracy (Private) 88.5 # 1
Zero-Shot Learning ImageNet_CN $M^2$-Encoder Accuracy 80.7 # 1

Methods