  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.
