Semantic Segmentation from Sparse Labeling Using Multi-Level Superpixels

Semantic segmentation is a challenging problem that can benefit numerous robotics applications, since it provides information about the content at every image pixel. Solutions to this problem have recently witnessed a boost on performance and results thanks to deep learning approaches. Unfortunately, common deep learning models for semantic segmentation present several challenges which hinder real life applicability in many domains. A significant challenge is the need of pixel level labeling on large amounts of training images to be able to train those models, which implies a very high cost. This work proposes and validates a simple but effective approach to train dense semantic segmentation models from sparsely labeled data. Labeling only a few pixels per image reduces the human interaction required. We find many available datasets, e.g., environment monitoring data, that provide this kind of sparse labeling. Our approach is based on augmenting the sparse annotation to a dense one with the proposed adaptive superpixel segmentation propagation. We show that this label augmentation enables effective learning of state-of-the-art segmentation models, getting similar results to those models trained with dense ground-truth. We demonstrate the applicability of the presented approach to different image modalities in real domains (underwater, aerial and urban scenarios) with publicly available datasets.