论文标题
基于注意的作家独立手写验证
Attention based Writer Independent Handwriting Verification
论文作者
论文摘要
作家验证的任务是为是否属于同一作者的查询和已知手写的图像样本提供了可能分数。这样的任务要求神经网络使其结果可以解释，即对网络的决策过程提供视图。我们实施并集成了交叉注意和软注意机制，以捕获2D输入的特征空间中高度相关和显着点。注意地图是网络输出可能性评分的解释前提。注意机制还使网络可以更多地关注输入的相关领域，从而提高分类性能。我们提出的方法可实现86 \％的精度，用于检测雪松草皮中的撰写者案例”和“数据集。此外，我们通过从网络的多个级别提取注意力图来为提供的决策产生有意义的解释。
The task of writer verification is to provide a likelihood score for whether the queried and known handwritten image samples belong to the same writer or not. Such a task calls for the neural network to make it's outcome interpretable, i.e. provide a view into the network's decision making process. We implement and integrate cross-attention and soft-attention mechanisms to capture the highly correlated and salient points in feature space of 2D inputs. The attention maps serve as an explanation premise for the network's output likelihood score. The attention mechanism also allows the network to focus more on relevant areas of the input, thus improving the classification performance. Our proposed approach achieves a precision of 86\% for detecting intra-writer cases in CEDAR cursive "AND" dataset. Furthermore, we generate meaningful explanations for the provided decision by extracting attention maps from multiple levels of the network.
