S$^{5}$Mars: Semi-Supervised Learning for Mars Semantic Segmentation

arXiv:2207.01200v4 Announce Type: replace
Abstract: Deep learning has become a powerful tool for Mars exploration. Mars terrain semantic segmentation is an important Martian vision task, which is the base of rover autonomous planning and safe driving. However, there is a lack of sufficient detailed and high-confidence data annotations, which are exactly required by most deep learning methods to obtain a good model. To address this problem, we propose our solution from the perspective of joint data and method design. We …

arxiv cs.cv mars segmentation semantic semi-supervised semi-supervised learning supervised learning type