Binary Segmentation Based Class Extension in Semantic Image Segmentation Using Convolutional Neural Networks

摘要

We deal with semantic image segmentation using deep convolutional neural networks (CNNs) and propose to extent a well-trained model to capture more classes. Because ground truth is very expensive in such a pixel-wise classification task, we avoid the manual annotation of the new classes by using a binary segmentation model to support the class extension. We use soft targets (probabilities), reuse and distill knowledge from the old segmentation model, and fuse information from the binary model to regularize the training of a new model with extended classes. In the experiments, we show that our method outperforms two other methods and improves the accuracy of small object classes. Moreover, our method is robust and more capable of tolerating bad binary models.