  Based on multiple instance detection networks (MIDN), plenty of works have
contributed tremendous efforts to weakly supervised object detection (WSOD).
However, most methods neglect the fact that the overwhelming negative instances
exist in each image during the training phase, which would mislead the training
and make the network fall into local minima. To tackle this problem, an online
progressive instance-balanced sampling (OPIS) algorithm based on hard sampling
and soft sampling is proposed in this paper. The algorithm includes two
modules: a progressive instance balance (PIB) module and a progressive instance
reweighting (PIR) module. The PIB module combining random sampling and
IoU-balanced sampling progressively mines hard negative instances while
balancing positive instances and negative instances. The PIR module further
utilizes classifier scores and IoUs of adjacent refinements to reweight the
weights of positive instances for making the network focus on positive
instances. Extensive experimental results on the PASCAL VOC 2007 and 2012
datasets demonstrate the proposed method can significantly improve the
baseline, which is also comparable to many existing state-of-the-art results.
In addition, compared to the baseline, the proposed method requires no extra
network parameters and the supplementary training overheads are small, which
could be easily integrated into other methods based on the instance classifier
refinement paradigm.
