# Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation Source: [Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2026-01-02T13:01:48.603Z Authors: Beomyoung Kim, Sangeun Han, Junmo Kim Published: 2021-03-12T12:56:06.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on Pascal VOC (test)](): Semantic segmentation; Pascal VOC (test); mIoU: 71.4. - [Semantic Segmentation on PASCAL VOC (val)](): Semantic segmentation; PASCAL VOC (val); mIoU: 71.2. - [Semantic Segmentation on PASCAL VOC 2012 (test)](): Semantic segmentation; PASCAL VOC 2012 (test); mIoU: 71.4. - [Semantic Segmentation on PASCAL VOC 2012 (val)](): Semantic segmentation; PASCAL VOC 2012 (val); Mean IoU: 71.2. - [Weakly Supervised Semantic Segmentation on PASCAL VOC 2012 (val)](): Weakly supervised semantic segmentation; PASCAL VOC 2012 (val); mIoU: not recorded. - [Weakly Supervised Semantic Segmentation on PASCAL VOC 2012 (test)](): Weakly supervised semantic segmentation; PASCAL VOC 2012 (test); mIoU: not recorded. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()