Context-aware methods achieved great success in supervised scene text
recognition via incorporating semantic priors from words. We argue that such
prior contextual information can be interpreted as the relations of textual
primitives due to the heterogeneous text and background, which can provide
effective self-supervised labels for representation learning. However, textual
relations are restricted to the finite size of dataset due to lexical
dependencies, which causes the problem of over-fitting and compromises
representation robustness. To this end, we propose to enrich the textual
relations via rearrangement, hierarchy and interaction, and design a unified
framework called RCLSTR: Relational Contrastive Learning for Scene Text
Recognition. Based on causality, we theoretically explain that three modules
suppress the bias caused by the contextual prior and thus guarantee
representation robustness. Experiments on representation quality show that our
method outperforms state-of-the-art self-supervised STR methods. Code is
available at this https URL

通过处理上下文信息和文本关系，研究论文提出一种名为 RCLSTR 的统一框架，用于景观文本识别的关联对比学习，该框架通过重新排列、层次和交互来丰富文本关系，解决了上下文信息限制和过拟合问题，提高了表示鲁棒性。实验结果证明了该方法在表示质量上优于最先进的自监督 STR 方法。

场景文本识别的关系对比学习

Relational Contrastive Learning for Scene Text Recognition

Pre-trained embeddings such as word embeddings and sentence embeddings are
fundamental tools facilitating a wide range of downstream NLP tasks. In this
work, we investigate how to learn a general-purpose embedding of textual
relations, defined as the shortest dependency path between entities. Textual
relation embedding provides a level of knowledge between word/phrase level and
sentence level, and we show that it can facilitate downstream tasks requiring
relational understanding of the text. To learn such an embedding, we create the
largest distant supervision dataset by linking the entire English ClueWeb09
corpus to Freebase. We use global co-occurrence statistics between textual and
knowledge base relations as the supervision signal to train the embedding.
Evaluation on two relational understanding tasks demonstrates the usefulness of
the learned textual relation embedding. The data and code can be found at
this https URL

本研究探讨了如何学习文本关系的通用嵌入，通过创建最大的远距离监督数据集并使用全局共现统计来训练嵌入，证明了学习文本关系嵌入的效用，可以促进需要文本关系理解的下游任务。