The learning objective of vision-language approach of CLIP does not effectively account for the noisy many-to-many correspondences found in web-harvested image captioning datasets, which contributes to its compute and data inefficiency. To address this challenge, we introduce a novel training framework based on cross-modal contrastive learning that uses progressive self-distillation and soft image-text alignments to more efficiently learn robust representations from noisy data. Our model distills its own knowledge to dynamically generate soft-alignment targets for a subset of images and captions in every minibatch, which are then used to update its parameters. Extensive evaluation across 14 benchmark datasets shows that our method consistently outperforms its CLIP counterpart in multiple settings, including: (a) zero-shot classification, (b) linear probe transfer, and (c) image-text retrieval, without incurring added computational cost. Analysis using an ImageNet-based robustness test-bed reveals that our method offers better effective robustness to natural distribution shifts compared to both ImageNet-trained models and CLIP itself. Lastly, pretraining with datasets spanning two orders of magnitude in size shows that our improvements over CLIP tend to scale with number of training examples.

通过交叉模态对比学习以及软图像-文本对齐等方法，改进了CLIP模型，在处理带有噪声的数据集时能更加高效地学习出具有鲁棒性的表示。经过对14个基准数据集的广泛评估，该方法在多种设置下表现均优于CLIP，并且没有增加计算成本。此外，该方法还在自然分布偏移的鲁棒性测试中表现更好。

稳健的跨模态表示学习与渐进式自蒸馏