特征混合用于纸莎草残片上的书写者检索与识别
Feature Mixing for Writer Retrieval and Identification on Papyri Fragments
AI总结：
本文提出一种结合残差主干与特征混合的深度学习架构，用于纸莎草残片的书写者检索与识别，在PapyRow和HisFragIR20上取得领先性能，并发现二值化无益。
摘要：
本文提出了一种基于深度学习的方法，用于纸莎草残片的书写者检索与识别，重点关注识别与特定书写者相关的残片以及对应于同一图像的残片。我们提出了一种新颖的神经网络架构，将残差主干网络与特征混合阶段相结合，以提高检索性能，最终描述符由投影层生成。该方法在两个基准数据集上进行了评估：在PapyRow上，我们在书写者检索和页面检索上分别取得了26.6%和24.9%的平均精度（mAP）；在HisFragIR20上，展示了最先进的性能（44.0%和29.3%的mAP）。此外，我们的网络在书写者识别上的准确率为28.7%。我们还对两种二值化技术对残片的影响进行了实验，结果表明二值化并未提升性能。我们的代码和模型已向社区公开。
英文摘要：
This paper proposes a deep-learning-based approach to writer retrieval and identification for papyri, with a focus on identifying fragments associated with a specific writer and those corresponding to the same image. We present a novel neural network architecture that combines a residual backbone with a feature mixing stage to improve retrieval performance, and the final descriptor is derived from a projection layer. The methodology is evaluated on two benchmarks: PapyRow, where we achieve a mAP of 26.6 % and 24.9 % on writer and page retrieval, and HisFragIR20, showing state-of-the-art performance (44.0 % and 29.3 % mAP). Furthermore, our network has an accuracy of 28.7 % for writer identification. Additionally, we conduct experiments on the influence of two binarization techniques on fragments and show that binarizing does not enhance performance. Our code and models are available to the community.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
