用于文档验证的循环少样本模型
Recurrent Few-Shot model for Document Verification
AI总结：
本文提出一种基于循环架构的少样本模型，用于在低分辨率和数据不足条件下检测未见类别身份证件或旅行证件中的伪造文档，并在SIDTD和Findit数据集上取得良好初步效果。
摘要：
通用的身份证件或旅行证件图像与视频验证系统尚未达到足够好的性能，因此不能被视为已解决的问题。有若干因素对其性能产生负面影响，包括低分辨率图像和视频，以及缺乏足够的数据来训练模型。当处理未见过的身份证件或旅行证件类别时，该任务尤其具有挑战性。在本文中，我们通过提出一种基于循环架构的模型来解决这一任务，该模型能够在少样本场景下检测伪造文档。循环架构使模型对文档分辨率变化具有鲁棒性。此外，少样本方法使模型即使对未见过的文档类别也能表现良好。在SIDTD和Findit数据集上的初步结果表明，该模型在此任务上具有良好的性能。
英文摘要：
General-purpose ID, or travel, document image- and video-based verification systems have yet to achieve good enough performance to be considered a solved problem. There are several factors that negatively impact their performance, including low-resolution images and videos and a lack of sufficient data to train the models. This task is particularly challenging when dealing with unseen class of ID, or travel, documents. In this paper we address this task by proposing a recurrent-based model able to detect forged documents in a few-shot scenario. The recurrent architecture makes the model robust to document resolution variability. Moreover, the few-shot approach allow the model to perform well even for unseen class of documents. Preliminary results on the SIDTD and Findit datasets show good performance of this model for this task.
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。
