  Based on the framework of multiple instance learning (MIL), tremendous works
have promoted the advances of weakly supervised object detection (WSOD).
However, most MIL-based methods tend to localize instances to their
discriminative parts instead of the whole content. In this paper, we propose a
spatial likelihood voting (SLV) module to converge the proposal localizing
process without any bounding box annotations. Specifically, all region
proposals in a given image play the role of voters every iteration during
training, voting for the likelihood of each category in spatial dimensions.
After dilating alignment on the area with large likelihood values, the voting
results are regularized as bounding boxes, being used for the final
classification and localization. Based on SLV, we further propose an end-to-end
training framework for multi-task learning. The classification and localization
tasks promote each other, which further improves the detection performance.
Extensive experiments on the PASCAL VOC 2007 and 2012 datasets demonstrate the
superior performance of SLV.
