基于对比学习的双全局描述子狗鼻纹匹配
Dog nose print matching with dual global descriptor based on Contrastive Learning
摘要：
最近在基于生物识别的识别任务中的研究表明，深度学习方法可以实现更好的性能。这些方法一般提取全局特征作为描述符来表示原始图像。尽管如此，它在细粒度任务下的生物识别方面表现不佳。其主要原因是单一的图像描述符所包含的信息不足以表示图像。本文提出了一种双全局描述子模型，该模型结合多个全局描述子来开发多层次的图像特征。此外，我们利用对比损失扩大混淆类的图像表示之间的距离。该框架在CVPR2022生物识别研讨会宠物生物识别挑战赛上获得了前2名。源代码和经过训练的模型可在以下网址公开获取：https://github.com/flyingsheepbin/pet-biometrics
英文摘要：
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
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。
