  The recent advances of convolutional detectors show impressive performance
improvement for large scale object detection. However, in general, the
detection performance usually decreases as the object classes to be detected
increases, and it is a practically challenging problem to train a dominant
model for all classes due to the limitations of detection models and datasets.
In most cases, therefore, there are distinct performance differences of the
modern convolutional detectors for each object class detection. In this paper,
in order to build an ensemble detector for large scale object detection, we
present a conceptually simple but very effective class-wise ensemble detection
which is named as Rank of Experts. We first decompose an intractable problem of
finding the best detections for all object classes into small subproblems of
finding the best ones for each object class. We then solve the detection
problem by ranking detectors in order of the average precision rate for each
class, and then aggregate the responses of the top ranked detectors (i.e.
experts) for class-wise ensemble detection. The main benefit of our method is
easy to implement and does not require any joint training of experts for
ensemble. Based on the proposed Rank of Experts, we won the 2nd place in the
ILSVRC 2017 object detection competition.
