Camera trapping is increasingly used to monitor wildlife, but this technology typically requires extensive data annotation. Recently, deep learning has significantly advanced automatic wildlife recognition. However, current methods are hampered by a dependence on large static data sets when wildlife data is intrinsically dynamic and involves long-tailed distributions. These two 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 that facilitates capturing the community dynamics of rapidly changing natural systems. Extensive experiments show that our approach can achieve a ~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 on-going annotation tool that vastly relieves the burden of human annotation and enables efficient and constant model updates.

中文翻译：

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人工和自动迭代识别野生生物图像

摄像头捕获越来越多地用于监视野生动植物，但是该技术通常需要大量的数据注释。最近，深度学习已大大提高了野生动植物自动识别的能力。但是，当野生生物数据本质上是动态的并且涉及长尾分布时，当前方法由于依赖大型静态数据集而受到阻碍。这两个缺点可以通过循环中机器学习和人员的混合克服。我们提出的迭代式人工和自动识别方法能够从具有长尾分布的野生动植物图像数据中学习。此外，它还包括自我更新学习，可帮助捕获迅速变化的自然系统的社区动态。大量的实验表明，我们的方法仅使用现有方法的约20％的人类注释即可达到约90％的准确度。我们的人机协同协作将深度学习从相对低效的后期注释工具转变为持续进行中的协作注释工具，从而极大地减轻了人工注释的负担并实现了有效且持续的模型更新。