End-to-end weakly supervised semantic segmentation with reliable region mining

Highlights：

• We make an exten sion of our previous wok and design a more powerful end to end n etwork for weakly supervised semantic segmentation

• We propose two new loss functions for utilizing the reliable labels, including a new dense energy loss and a batch based class distance loss. The former relies on shallow features, whilst the latter focuses on distinguishing high level s emantic features for different classes.

• We design a new attention module to extract comprehensive global information. By using a re weighting technique, it can suppress dominant or noisy attention values and aggregate sufficient global information.

• Our approach achieves a new state of the art performance for weakly supervised semantic segmentation.