Camera trapping is increasingly being used to monitor wildlife, but this technology typically requires extensive data annotation. Recently, deep learning has substantially advanced automatic wildlife recognition. However, current methods are hampered by a dependence on large static datasets, whereas wildlife data are intrinsically dynamic and involve long-tailed distributions. These drawbacks can be overcome through a hybrid combination of machine learning and humans in the loop. Our proposed iterative human and automated identification approach is capable of learning from wildlife imagery data with a long-tailed distribution. Additionally, it includes self-updating learning, which facilitates capturing the community dynamics of rapidly changing natural systems. Extensive experiments show that our approach can achieve an ~90% accuracy employing only ~20% of the human annotations of existing approaches. Our synergistic collaboration of humans and machines transforms deep learning from a relatively inefficient post-annotation tool to a collaborative ongoing annotation tool that vastly reduces the burden of human annotation and enables efficient and constant model updates.

相机捕捉越来越多地用于监测野生动物，但这项技术通常需要大量的数据注释。最近，深度学习大大提高了自动野生动物识别的能力。然而，当前的方法受到对大型静态数据集的依赖的阻碍，而野生动物数据本质上是动态的并且涉及长尾分布。这些缺点可以通过机器学习和人在循环中的混合组合来克服。我们提出的迭代人工和自动识别方法能够从具有长尾分布的野生动物图像数据中学习。此外，它还包括自我更新学习，这有助于捕捉快速变化的自然系统的社区动态。大量实验表明，我们的方法仅使用现有方法中约 20% 的人工注释即可达到约 90% 的准确度。我们人类和机器的协同协作将深度学习从相对低效的后注释工具转变为协作的持续注释工具，极大地减少了人类注释的负担，并实现了高效和持续的模型更新。