TwinsAdvNet : Adversarial Learning for Semantic Segmentation Recently, many semantic segmentation networks provide more training samples by generative adversarial networks. In practice, the generator does not simulate the detail textures of natural images, and image semantics segmentation network often has the problem of inconsistent prediction results due to small changes in network structure. In view of the above problems and the task of understanding complex scenarios, it is considered that the existing generators are not suitable for providing real training samples, but should be based on providing valuable label semantic information. Therefore, a twobranch semantic segmentation network, called TwinsAdvNet, which uses two kinds of predictive probability map to adversarial learning, is proposed. Its novelty is that the prediction results of one branch of segmented network are regarded as weak labels of another branch of semantic segmentation. Experimental results on MIT Scene Parsing Benchmark dataset are included to demonstrate the effectiveness of the proposed model.

Recognizing novel patterns via adversarial learning for oneshot semantic segmentation Abstract   Oneshot semantic segmentation aims to recognize unseen object regions by using the reference of only one annotated example. Many deep convolutional neural networks have achieved enormous success on this task. However, most of the existing methods only use a fixed annotated dataset to train the network. The remaining unannotated examples remain difficult to be leveraged and recognized. In this study, we propose a procedure based on the generative adversarial network to enable the oneshot semantic segmentation model for learning information from previously unknown categories. Our method contains a segmentation network that generates segmentation predictions. We then use a discriminator to differentiate the probability maps of segmentation prediction from the ground truth distribution. Consequently, we can ignore the pixels classified as fake and only use trustworthy regions as the label to train the segmentation network, thus achieving semisupervised learning. Experimental results demonstrate the effectiveness of the proposed adversarial learning method with an average gain of 49.7% accuracy score on the PASCAL VOC 2012 dataset.