Few-Shot Semantic Segmentation based on Detail-Preserving-Aware Loss

The vision-based autonomous driving techniques have been activated recently. The pixel-level prediction, semantic segmentation, is widely used in autonomous driving to acquire the pixel-level semantic annotation for further decision-making. However, traditional semantic segmentation requires a large training set to obtain promising performance. It is essential that it is hard to collect such a large dataset for every scenario for autonomous driving, such as outdoor or other bad weather scenarios. In this paper, we propose a novel boundary-aware loss incorporating the rare object augmentation techniques to boost the performance under a limited training set. The conventional edge extraction operator is applied in the ground truth to obtain the boundary information, as well as the detailed branch is proposed to approximate the predicted results to have a similar boundary with the ground truth. Such hard constraints result in the network being hard to overfit, as well as improve the robustness of the semantic segmentation. Extensive experiments demonstrated the proposed method's effectiveness, especially in limited training set scenarios.