Writer identification due to its widespread application in various fields has
gained popularity over the years. In scenarios where optimum handwriting
samples are available, whether they be in the form of a single line, a
sentence, or an entire page, writer identification algorithms have demonstrated
noteworthy levels of accuracy. However, in scenarios where only a limited
number of handwritten samples are available, particularly in the form of word
images, there is a significant scope for improvement.
In this paper, we propose a writer identification system based on an
attention-driven Convolutional Neural Network (CNN). The system is trained
utilizing image segments, known as fragments, extracted from word images,
employing a pyramid-based strategy. This methodology enables the system to
capture a comprehensive representation of the data, encompassing both
fine-grained details and coarse features across various levels of abstraction.
These extracted fragments serve as the training data for the convolutional
network, enabling it to learn a more robust representation compared to
traditional convolution-based networks trained on word images. Additionally,
the paper explores the integration of an attention mechanism to enhance the
representational power of the learned features. The efficacy of the proposed
algorithm is evaluated on three benchmark databases, demonstrating its
proficiency in writer identification tasks, particularly in scenarios with
limited access to handwriting data.

本文提出了一种基于注意力驱动的卷积神经网络的作者识别系统，该系统利用从词图像中提取的图像片段进行训练，采用金字塔策略。该系统能够全面捕捉数据的细节和粗糙特征，以及不同抽象层次上的信息。此外，文中探讨了使用注意力机制来增强学习特征的表征能力。该算法在三个基准数据库上进行了评估，证明了其在作者识别任务中的有效性，尤其是在有限手写数据的情景中。

基于注意力机制的离线书写者识别端到端网络 (基于字级数据)

Attention based End to end network for Offline Writer Identification on  Word level data

Existing works on semantic segmentation typically consider a small number of
labels, ranging from tens to a few hundreds. With a large number of labels,
training and evaluation of such task become extremely challenging due to
correlation between labels and lack of datasets with complete annotations. We
formulate semantic segmentation as a problem of image segmentation given a
semantic concept, and propose a novel system which can potentially handle an
unlimited number of concepts, including objects, parts, stuff, and attributes.
We achieve this using a weakly and semi-supervised framework leveraging
multiple datasets with different levels of supervision. We first train a deep
neural network on a 6M stock image dataset with only image-level labels to
learn visual-semantic embedding on 18K concepts. Then, we refine and extend the
embedding network to predict an attention map, using a curated dataset with
bounding box annotations on 750 concepts. Finally, we train an attention-driven
class agnostic segmentation network using an 80-category fully annotated
dataset. We perform extensive experiments to validate that the proposed system
performs competitively to the state of the art on fully supervised concepts,
and is capable of producing accurate segmentations for weakly learned and
unseen concepts.

本论文提出了一种新的基于弱监督和半监督框架的语义分割系统，可以处理包括对象、部分、杂物和属性在内的无限数量的概念，通过训练深度神经网络，产生视觉 - 语义嵌入，在完全注释的数据集上训练注意力驱动类无关分割网络。