  Label distributions in camera-trap images are highly imbalanced and
long-tailed, resulting in neural networks tending to be biased towards
head-classes that appear frequently. Although long-tail learning has been
extremely explored to address data imbalances, few studies have been conducted
to consider camera-trap characteristics, such as multi-domain and multi-frame
setup. Here, we propose a unified framework and introduce two datasets for
long-tailed camera-trap recognition. We first design domain experts, where each
expert learns to balance imperfect decision boundaries caused by data
imbalances and complement each other to generate domain-balanced decision
boundaries. Also, we propose a flow consistency loss to focus on moving
objects, expecting class activation maps of multi-frame matches the flow with
optical flow maps for input images. Moreover, two long-tailed camera-trap
datasets, WCS-LT and DMZ-LT, are introduced to validate our methods.
Experimental results show the effectiveness of our framework, and proposed
methods outperform previous methods on recessive domain samples.
