Title: Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach,
Abstract: We investigate a principle way to progressively mine discriminative object
regions using classification networks to address the weakly-supervised semantic
segmentation problems. Classification networks are only responsive to small and
sparse discriminative regions from the object of interest, which deviates from
the requirement of the segmentation task that needs to localize dense, interior
and integral regions for pixel-wise inference. To mitigate this gap, we propose
a new adversarial erasing approach for localizing and expanding object regions
progressively. Starting with a single small object region, our proposed
approach drives the classification network to sequentially discover new and
complement object regions by erasing the current mined regions in an
adversarial manner. These localized regions eventually constitute a dense and
complete object region for learning semantic segmentation. To further enhance
the quality of the discovered regions by adversarial erasing, an online
prohibitive segmentation learning approach is developed to collaborate with
adversarial erasing by providing auxiliary segmentation supervision modulated
by the more reliable classification scores. Despite its apparent simplicity,
the proposed approach achieves 55.0% and 55.7% mean Intersection-over-Union
(mIoU) scores on PASCAL VOC 2012 val and test sets, which are the new
state-of-the-arts.