39 research outputs found
Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation
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 https://github.com/qjadud1994/DRS.Comment: AAAI 2021, Accepte