To facilitate the re-identification (Re-ID) of individual animals, existing methods primarily focus on maximizing feature similarity within the same individual and enhancing distinctiveness between different individuals. However, most of them still rely on supervised learning and require substantial labeled data, which is challenging to obtain. To avoid this issue, we propose a Feature-Aware Noise Contrastive Learning (FANCL) method to explore an unsupervised learning solution, which is then validated on the task of red panda re-ID. FANCL employs a Feature-Aware Noise Addition module to produce noised images that conceal critical features and designs two contrastive learning modules to calculate the losses. Firstly, a feature consistency module is designed to bridge the gap between the original and noised features. Secondly, the neural networks are trained through a cluster contrastive learning module. Through these more challenging learning tasks, FANCL can adaptively extract deeper representations of red pandas. The experimental results on a set of red panda images collected in both indoor and outdoor environments prove that FANCL outperforms several related state-of-the-art unsupervised methods, achieving high performance comparable to supervised learning methods.

翻译：为了促进个体动物的重识别（Re-ID），现有方法主要侧重于最大化同一动物个体内的特征相似性，并增强不同个体间的特征区分度。然而，大多数方法仍依赖于监督学习，需要大量难以获取的标注数据。为避免此问题，我们提出一种特征感知噪声对比学习（FANCL）方法，探索无监督学习解决方案，并在小熊猫重识别任务上进行验证。FANCL采用特征感知噪声添加模块生成掩盖关键特征的噪声图像，并设计两个对比学习模块来计算损失。首先，设计特征一致性模块以缩小原始特征与噪声特征之间的差距。其次，通过聚类对比学习模块训练神经网络。通过这些更具挑战性的学习任务，FANCL能够自适应地提取小熊猫的更深层特征表示。在室内和室外环境中收集的一组小熊猫图像上的实验结果表明，FANCL优于多项相关的最新无监督方法，取得了与监督学习方法相媲美的高性能。