  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.
