Har Enhanced Weakly-Supervised Semantic Segmentation Coupled with Adversarial Learning

Abstract

Semantic segmentation is a challenging computer visual task which needs enormous pixel-level annotation data. But collecting a large amount of pixel-level annotation data is labor intensive. To address this issue, our work focuses on weakly-supervised learning approach which combines the adversarial learning and localization ability of classification model together, in this way, data with different annotations can be fully utilized. Specifically, the adversarial learning encourages the high order spatial consistences thus offers a relatively reliable initial confidence map. And we find that the hybrid atrous rate (HAR) can improve the localization ability of the classification model, thus indicate more precise object-related regions, which serves as strong supervision information. We conduct experiments with different settings to demonstrate the effectiveness of this weakly-supervised learning approach. The results show that our approach can improve the performance of baseline adversarial learning from 73.2 to 75.1 (mIOU), which is pretty effective.