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

Textual adversarial attacks expose the vulnerabilities of text classifiers
and can be used to improve their robustness. Existing context-aware methods
solely consider the gold label probability and use the greedy search when
searching an attack path, often limiting the attack efficiency. To tackle these
issues, we propose PDBS, a context-aware textual adversarial attack model using
Probability Difference guided Beam Search. The probability difference is an
overall consideration of all class label probabilities, and PDBS uses it to
guide the selection of attack paths. In addition, PDBS uses the beam search to
find a successful attack path, thus avoiding suffering from limited search
space. Extensive experiments and human evaluation demonstrate that PDBS
outperforms previous best models in a series of evaluation metrics, especially
bringing up to a +19.5% attack success rate. Ablation studies and qualitative
analyses further confirm the efficiency of PDBS.

提出了一种名为 PDBS 的新的上下文感知的文本对抗攻击模型，它使用概率差异对攻击路径进行选择，并使用波束搜索来寻找成功的攻击路径，实验证明 PDBS 在一系列评估指标上优于以前的最佳模型，特别是攻击成功率提高了 19.5%。

一种基于概率差异引导的束搜索文本对抗攻击的上下文感知方法

A Context-Aware Approach for Textual Adversarial Attack through Probability Difference Guided Beam Search

Neural Machine Translation (NMT) can be improved by including document-level
contextual information. For this purpose, we propose a hierarchical attention
model to capture the context in a structured and dynamic manner. The model is
integrated in the original NMT architecture as another level of abstraction,
conditioning on the NMT model's own previous hidden states. Experiments show
that hierarchical attention significantly improves the BLEU score over a strong
NMT baseline with the state-of-the-art in context-aware methods, and that both
the encoder and decoder benefit from context in complementary ways.

本文提出了一种层次化的注意力模型，通过在神经机器翻译中引入文本级的上下文信息，有效提升了翻译的质量。实验结果表明，编码器和解码器都对上下文信息做出了积极贡献。