  This paper addresses weakly supervised object detection with only image-level
supervision at training stage. Previous approaches train detection models with
entire images all at once, making the models prone to being trapped in
sub-optimums due to the introduced false positive examples. Unlike them, we
propose a zigzag learning strategy to simultaneously discover reliable object
instances and prevent the model from overfitting initial seeds. Towards this
goal, we first develop a criterion named mean Energy Accumulation Scores (mEAS)
to automatically measure and rank localization difficulty of an image
containing the target object, and accordingly learn the detector progressively
by feeding examples with increasing difficulty. In this way, the model can be
well prepared by training on easy examples for learning from more difficult
ones and thus gain a stronger detection ability more efficiently. Furthermore,
we introduce a novel masking regularization strategy over the high level
convolutional feature maps to avoid overfitting initial samples. These two
modules formulate a zigzag learning process, where progressive learning
endeavors to discover reliable object instances, and masking regularization
increases the difficulty of finding object instances properly. We achieve 47.6%
mAP on PASCAL VOC 2007, surpassing the state-of-the-arts by a large margin.
