论文标题
基于对比度学习，狗鼻打印与双重全球描述符
Dog nose print matching with dual global descriptor based on Contrastive Learning
论文作者
论文摘要
基于生物特征识别的识别任务的最新研究表明，深度学习方法可以实现更好的性能。这些方法通常将全局特征作为描述符提取以表示原始图像。尽管如此，在细粒度的任务下，它对于生物识别识别表现不佳。主要原因是单个图像描述符包含不足以表示图像的信息。在本文中，我们提出了双重全局描述符模型，该模型结合了多个全局描述符来利用多级图像特征。此外，我们利用对比度损失来扩大混淆类的图像表示之间的距离。提出的框架在CVPR2022生物识别研讨会宠物生物识别挑战上实现了TOP2。源代码和训练有素的模型可公开可用：https：//github.com/flyingsheepbin/pet-biometrics
Recent studies in biometric-based identification tasks have shown that deep learning methods can achieve better performance. These methods generally extract the global features as descriptor to represent the original image. Nonetheless, it does not perform well for biometric identification under fine-grained tasks. The main reason is that the single image descriptor contains insufficient information to represent image. In this paper, we present a dual global descriptor model, which combines multiple global descriptors to exploit multi level image features. Moreover, we utilize a contrastive loss to enlarge the distance between image representations of confusing classes. The proposed framework achieves the top2 on the CVPR2022 Biometrics Workshop Pet Biometric Challenge. The source code and trained models are publicly available at: https://github.com/flyingsheepbin/pet-biometrics
