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.

该研究提出了一种基于自产引导（SPG）蒙版的弱监督目标定位方法，使用 SPG 蒙版提供像素的空间相关信息，作为辅助的像素级监督来辅助分类网络的训练，实验证明 SPG 能够在提高目标定位精度方面显著有效。