  Weakly supervised methods usually generate localization results based on
attention maps produced by classification networks. However, the attention maps
exhibit the most discriminative parts of the object which are small and sparse.
We propose to generate Self-produced Guidance (SPG) masks which separate the
foreground, the object of interest, from the background to provide the
classification networks with spatial correlation information of pixels. A
stagewise approach is proposed to incorporate high confident object regions to
learn the SPG masks. The high confident regions within attention maps are
utilized to progressively learn the SPG masks. The masks are then used as an
auxiliary pixel-level supervision to facilitate the training of classification
networks. Extensive experiments on ILSVRC demonstrate that SPG is effective in
producing high-quality object localizations maps. Particularly, the proposed
SPG achieves the Top-1 localization error rate of 43.83% on the ILSVRC
validation set, which is a new state-of-the-art error rate.
