Recently, adversarial erasing for weakly-supervised object attention has been deeply studied due to its capability in localizing integral object regions. However, such a strategy raises one key problem that attention regions will gradually expand to non-object regions as training iterations continue, which significantly decreases the quality of the produced attention maps. To tackle such an issue as well as promote the quality of object attention, we introduce a simple yet effective Self-Erasing Network (SeeNet) to prohibit attentions from spreading to unexpected background regions. In particular, SeeNet leverages two self-erasing strategies to encourage networks to use reliable object and background cues for learning to attention. In this way, integral object regions can be effectively highlighted without including much more background regions. To test the quality of the generated attention maps, we employ the mined object regions as heuristic cues for learning semantic segmentation models. Experiments on Pascal VOC well demonstrate the superiority of our SeeNet over other state-of-the-art methods.

本文介绍了一种自擦除的网络（SeeNet），用于解决弱监督目标注意力定位中注意力区域逐渐扩散到非目标区域的问题，以及促进目标注意质量，其基于两种自我擦除策略，可以有效地突出积分对象区域，而不包括更多的背景区域。实验结果表明，相比其他最先进的方法，我们的SeeNet在Pascal VOC数据集上的表现更卓越。

自擦除网络用于整体物体关注