Vision-language pretraining models have achieved great success in supporting multimedia applications by understanding the alignments between images and text. While existing vision-language pretraining models primarily focus on understanding single image associated with a single piece of text, they often ignore the alignment at the intra-document level, consisting of multiple sentences with multiple images. In this work, we propose DocumentCLIP, a salience-aware contrastive learning framework to enforce vision-language pretraining models to comprehend the interaction between images and longer text within documents. Our model is beneficial for the real-world multimodal document understanding like news article, magazines, product descriptions, which contain linguistically and visually richer content. To the best of our knowledge, we are the first to explore multimodal intra-document links by contrastive learning. In addition, we collect a large Wikipedia dataset for pretraining, which provides various topics and structures. Experiments show DocumentCLIP not only outperforms the state-of-the-art baselines in the supervised setting, but also achieves the best zero-shot performance in the wild after human evaluation. Our code is available at https://github.com/FuxiaoLiu/DocumentCLIP.

A network is composed of a set of nodes, or vertices, with connections between them. Network data arise in a wide range of fields, and include social networks, collaboration networks, communication networks, biological networks, food webs and are a useful way of representing interactions between sets of objects. Of particular importance is the elaboration of random graph models, which can capture the salient properties of real-world graphs. Following the seminal work of Erd os and R enyi (1959), various network models have been proposed; see the overviews of Newman (2003b, 2009), Kolaczyk (2009), Bollob as (2001), Goldenberg et al. (2010), Fienberg (2012) or Jacobs and Clauset (2014). In particular, a large body of the literature has concentrated on models that can capture some modular or community structure within the network. The first statistical network model in this line of research is the popular stochastic block-model (Holland et al., 1983; Snijders and Nowicki, 1997; Nowicki and Snijders, 2001). The stochastic block-model assumes that each node belongs to one ofp latent communities, and the probability of connection between two nodes is given by ap p connectivity matrix. This model has been extended in various directions, by introducing degree-correction parameters (Karrer and Newman, 2011), by allowing the number of communities to grow with the size of the network (Kemp et al., 2006), or by considering overlapping communities (Airoldi et al., 2008; Miller et al., 2009; Latouche et al., 2011; Palla et al., 2012; Yang and Leskovec, 2013). Stochastic block-models and their extensions have shown to offer a very flexible modeling framework, with interpretable parameters, and have been successfully used for the analysis of numerous real-world networks.