  This paper presents a novel method for rare event detection from an image
pair with class-imbalanced datasets. A straightforward approach for event
detection tasks is to train a detection network from a large-scale dataset in
an end-to-end manner. However, in many applications such as building change
detection on satellite images, few positive samples are available for the
training. Moreover, scene image pairs contain many trivial events, such as in
illumination changes or background motions. These many trivial events and the
class imbalance problem lead to false alarms for rare event detection. In order
to overcome these difficulties, we propose a novel method to learn disentangled
representations from only low-cost negative samples. The proposed method
disentangles different aspects in a pair of observations: variant and invariant
factors that represent trivial events and image contents, respectively. The
effectiveness of the proposed approach is verified by the quantitative
evaluations on four change detection datasets, and the qualitative analysis
shows that the proposed method can acquire the representations that disentangle
rare events from trivial ones.
