  Though adversarial erasing has prevailed in weakly supervised semantic
segmentation to help activate integral object regions, existing approaches
still suffer from the dilemma of under-activation and over-expansion due to the
difficulty in determining when to stop erasing. In this paper, we propose a
\textbf{K}nowledge \textbf{T}ransfer with \textbf{S}imulated Inter-Image
\textbf{E}rasing (KTSE) approach for weakly supervised semantic segmentation to
alleviate the above problem. In contrast to existing erasing-based methods that
remove the discriminative part for more object discovery, we propose a
simulated inter-image erasing scenario to weaken the original activation by
introducing extra object information. Then, object knowledge is transferred
from the anchor image to the consequent less activated localization map to
strengthen network localization ability. Considering the adopted bidirectional
alignment will also weaken the anchor image activation if appropriate
constraints are missing, we propose a self-supervised regularization module to
maintain the reliable activation in discriminative regions and improve the
inter-class object boundary recognition for complex images with multiple
categories of objects. In addition, we resort to intra-image erasing and
propose a multi-granularity alignment module to gently enlarge the object
activation to boost the object knowledge transfer. Extensive experiments and
ablation studies on PASCAL VOC 2012 and COCO datasets demonstrate the
superiority of our proposed approach. Source codes and models are available at
https://github.com/NUST-Machine-Intelligence-Laboratory/KTSE.
