Pioneering dual-encoder pre-training works (e.g., CLIP and ALIGN) have revealed the potential of aligning multi-modal representations with contrastive learning. However, these works require a tremendous amount of data and computational resources (e.g., billion-level web data and hundreds of GPUs), which prevent researchers with limited resources from reproduction and further exploration. To this end, we explore a stack of simple but effective heuristics, and provide a comprehensive training guidance, which allows us to conduct dual-encoder multi-modal representation alignment with limited resources. We provide a reproducible strong baseline of competitive results, namely ZeroVL, with only 14M publicly accessible academic datasets and 8 V100 GPUs. Additionally, we collect 100M web data for pre-training, and achieve comparable or superior results than state-of-the-art methods, further proving the effectiveness of our method on large-scale data. We hope that this work will provide useful data points and experience for future research in multi-modal pre-training. Our code and pre-trained models will be released to facilitate the research community.

本文提出了一种可在有限资源下进行双编码器多模态表示对齐的新方法，并证明该算法在大规模数据上的有效性。

受限资源下的对比视觉-语言预训练