  Weakly-supervised semantic segmentation (WSS) ensures high-quality
segmentation with limited data and excels when employed as input seed masks for
large-scale vision models such as Segment Anything. However, WSS faces
challenges related to minor classes since those are overlooked in images with
adjacent multiple classes, a limitation originating from the overfitting of
traditional expansion methods like Random Walk. We first address this by
employing unsupervised and weakly-supervised feature maps instead of
conventional methodologies, allowing for hierarchical mask enhancement. This
method distinctly categorizes higher-level classes and subsequently separates
their associated lower-level classes, ensuring all classes are correctly
restored in the mask without losing minor ones. Our approach, validated through
extensive experimentation, significantly improves WSS across five benchmarks
(VOC: 79.8\%, COCO: 53.9\%, Context: 49.0\%, ADE: 32.9\%, Stuff: 37.4\%),
reducing the gap with fully supervised methods by over 84\% on the VOC
validation set. Code is available at https://github.com/shjo-april/DHR.
