- Abstract: Recently, the convolutional neural networks (CNNs) have shown great success on semantic segmentation task. However, for practical applications such as autonomous driving, the popular supervised learning method faces two challenges: the demand of low computational complexity and the need of huge training dataset accompanied by ground truth. Our focus in this paper is semi-supervised learning. We wish to use both labeled and unlabeled data in the training process. A highly efficient semantic segmentation network is our platform, which achieves high segmentation accuracy at low model size and high inference speed. We propose a semi-supervised learning approach to improve segmentation accuracy by including extra images without labels. While most existing semi-supervised learning methods are designed based on the adversarial learning techniques, we present a new and different approach, which trains an auxiliary CNN network that validates labels (ground-truth) on the unlabeled images. Therefore, in the supervised training phase, both the segmentation network and the auxiliary network are trained using labeled images. Then, in the unsupervised training phase, the unlabeled images are segmented and a subset of image pixels are picked up by the auxiliary network; and then they are used as ground truth to train the segmentation network. Thus, at the end, all dataset images can be used for retraining the segmentation network to improve the segmentation results. We use Cityscapes and CamVid datasets to verify the effectiveness of our semi-supervised scheme, and our experimental results show that it can improve the mean IoU for about 1.2% to 2.9% on the challenging Cityscapes dataset.
- Keywords: deep learning, semi-supervised segmentation, semantic segmentation, CNN
- TL;DR: We design a two-branch semi-supervised segmentation system consisting of a segmentation network and an auxiliary CNN network that validates labels (ground-truth) on the unlabeled images