  Weakly supervised object detection (WSOD) focuses on training object detector
with only image-level annotations, and is challenging due to the gap between
the supervision and the objective. Most of existing approaches model WSOD as a
multiple instance learning (MIL) problem. However, we observe that the result
of MIL based detector is unstable, i.e., the most confident bounding boxes
change significantly when using different initializations. We quantitatively
demonstrate the instability by introducing a metric to measure it, and
empirically analyze the reason of instability. Although the instability seems
harmful for detection task, we argue that it can be utilized to improve the
performance by fusing the results of differently initialized detectors. To
implement this idea, we propose an end-to-end framework with multiple detection
branches, and introduce a simple fusion strategy. We further propose an
orthogonal initialization method to increase the difference between detection
branches. By utilizing the instability, we achieve 52.6% and 48.0% mAP on the
challenging PASCAL VOC 2007 and 2012 datasets, which are both the new
state-of-the-arts.
