Expansion and Shrinkage of Localization for Weakly-Supervised Semantic Segmentation. (arXiv:2209.07761v2 [cs.CV] UPDATED)

Generating precise class-aware pseudo ground-truths, a.k.a, class activation
maps (CAMs), is essential for weakly-supervised semantic segmentation. The
original CAM method usually produces incomplete and inaccurate localization
maps. To tackle with this issue, this paper proposes an Expansion and Shrinkage
scheme based on the offset learning in the deformable convolution, to
sequentially improve the recall and precision of the located object in the two
respective stages. In the Expansion stage, an offset learning branch in a
deformable convolution layer, referred as …

arxiv localization segmentation semantic shrinkage weakly-supervised