ZeroVL: A Strong Baseline for Aligning Vision-Language Representations with Limited Resources

• URL: http://arxiv.org/abs/2112.09331v1
• Date: Fri, 17 Dec 2021 05:40:28 GMT
• Title: ZeroVL: A Strong Baseline for Aligning Vision-Language Representations with Limited Resources
• Authors: Quan Cui, Boyan Zhou, Yu Guo, Weidong Yin, Hao Wu, Osamu Yoshie
• Abstract summary: We provide a comprehensive training guidance, which allows us to conduct dual-encoder multi-modal representation alignment with limited resources. We collect 100M web data for pre-training, and achieve comparable or superior results than state-of-the-art methods. Our code and pre-trained models will be released to facilitate the research community.
• Score: 13.30815073857842
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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