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Vision-language pretraining models have achieved great success in supportingmultimedia applications by understanding the alignments between images andtext. While existing vision-language pretraining models primarily focus onunderstanding single image associated with a single piece of text, they oftenignore the alignment at the intra-document level, consisting of multiplesentences with multiple images. In this work, we propose DocumentCLIP, asalience-aware contrastive learning framework to enforce vision-languagepretraining models to comprehend the interaction between images and longer textwithin documents. Our model is beneficial for the real-world multimodaldocument understanding like news article, magazines, product descriptions,which contain linguistically and visually richer content. To the best of ourknowledge, we are the first to explore multimodal intra-document links bycontrastive learning. In addition, we collect a large Wikipedia dataset forpretraining, which provides various topics and structures. Experiments showDocumentCLIP not only outperforms the state-of-the-art baselines in thesupervised setting, but also achieves the best zero-shot performance in thewild after human evaluation. Our code is available athttps://github.com/FuxiaoLiu/DocumentCLIP.

DocumentCLIP: Linking Figures and Main Body Text in Reflowed Documents