While large scale pre-training has achieved great achievements in bridging the gap between vision and language, it still faces several challenges. First, the cost for pre-training is expensive. Second, there is no efficient way to handle the data noise which degrades model performance. Third, previous methods only leverage limited image-text paired data, while ignoring richer single-modal data, which may result in poor generalization to single-modal downstream tasks. In this work, we propose an EfficientCLIP method via Ensemble Confident Learning to obtain a less noisy data subset. Extra rich non-paired single-modal text data is used for boosting the generalization of text branch. We achieve the state-of-the-art performance on Chinese cross-modal retrieval tasks with only 1/10 training resources compared to CLIP and WenLan, while showing excellent generalization to single-modal tasks, including text retrieval and text classification.
@article{arxiv.2109.04699,
title = {EfficientCLIP: Efficient Cross-Modal Pre-training by Ensemble Confident Learning and Language Modeling},
author = {Jue Wang and Haofan Wang and Jincan Deng and Weijia Wu and Debing Zhang},
journal= {arXiv preprint arXiv:2109.04699},
year = {2021}
}