Weakly-supervised semantic segmentation (WSSS) using image-level labels has
recently attracted much attention for reducing annotation costs. Existing WSSS
methods utilize localization maps from the classification network to generate
pseudo segmentation labels. However, since localization maps obtained from the
classifier focus only on sparse discriminative object regions, it is difficult
to generate high-quality segmentation labels. To address this issue, we
introduce discriminative region suppression (DRS) module that is a simple yet
effective method to expand object activation regions. DRS suppresses the
attention on discriminative regions and spreads it to adjacent
non-discriminative regions, generating dense localization maps. DRS requires
few or no additional parameters and can be plugged into any network.
Furthermore, we introduce an additional learning strategy to give a
self-enhancement of localization maps, named localization map refinement
learning. Benefiting from this refinement learning, localization maps are
refined and enhanced by recovering some missing parts or removing noise itself.
Due to its simplicity and effectiveness, our approach achieves mIoU 71.4% on
the PASCAL VOC 2012 segmentation benchmark using only image-level labels.
Extensive experiments demonstrate the effectiveness of our approach. The code
is available at this https URL

本文提出了一种减少标注成本的方法，利用弱监督语义分割技术和判别性区域抑制模块（DRS）生成高质量分割标签。DRS 可以扩展对象激活区域并产生稠密的本地化区域，同时引入本地化地图精化学习策略以使本地化地图得以精化。实验证明，该方法在 PASCAL VOC 2012 分割基准上达到了 71.4％的 mIoU。