论文标题
同意：大胆单词分类的上下文敏感变压器
CONSENT: Context Sensitive Transformer for Bold Words Classification
论文作者
论文摘要
我们提出同意，这是一个简单而有效的上下文敏感的变压器框架，用于与上下文相关的对象分类，内部可实现的端到端深度学习管道。我们在大胆单词检测的任务上展示了提出的框架，证明了最先进的结果。给定一个图像，其中包含未知字体类型的文本（例如Arial，Calibri，Helvetica），未知语言，以各种照明，角度失真和比例变化为单位，我们提取所有单词并使用上下文依赖于上下文依赖于上下文的二进制分类（即使用BOLD versus versus non-Bold），使用最终的transformer-dend transformer网络网络网络启动。为了证明我们的框架的可扩展性，我们通过训练模型来确定获胜者，以$ 2 $图片的序列描绘了手工姿势，我们通过训练模型来确定获胜者，从而证明了对岩石剪辑器游戏的最新结果的竞争成果。
We present CONSENT, a simple yet effective CONtext SENsitive Transformer framework for context-dependent object classification within a fully-trainable end-to-end deep learning pipeline. We exemplify the proposed framework on the task of bold words detection proving state-of-the-art results. Given an image containing text of unknown font-types (e.g. Arial, Calibri, Helvetica), unknown language, taken under various degrees of illumination, angle distortion and scale variation, we extract all the words and learn a context-dependent binary classification (i.e. bold versus non-bold) using an end-to-end transformer-based neural network ensemble. To prove the extensibility of our framework, we demonstrate competitive results against state-of-the-art for the game of rock-paper-scissors by training the model to determine the winner given a sequence with $2$ pictures depicting hand poses.
