Abstract. This paper addresses Weakly Supervised Object Localization
(WSOL) with only image-level supervision. We propose a Multi-view
Learning Localization Network (ML-LocNet) by incorporating multiview learning into a two-phase WSOL model. The multi-view learning
would benefit localization due to the complementary relationships among
the learned features from different views and the consensus property among the mined instances from each view. In the first phase, the representation is augmented by integrating features learned from multiple views,
and in the second phase, the model performs multi-view co-training to
enhance localization performance of one view with the help of instances
mined from other views, which thus effectively avoids early fitting. MLLocNet can be easily combined with existing WSOL models to further
improve the localization accuracy. Its effectiveness has been proved experimentally. Notably, it achieves 68.6% CorLoc and 49.7% mAP on
PASCAL VOC 2007, surpassing the state-of-the-arts by a large margin.