With the progress of Mars exploration, numerous Mars image data are collected and need to be analyzed. However, because of the imbalance and distortion in Mars data, the performance of existing classification models is unsatisfactory. In this paper, we design a new framework based on semi-supervised contrastive learning for Mars rover image classification. The redundancy of Mars data can disable the effectiveness of contrastive learning. To strip out problematic learning samples, we propose to ignore inner-class pairs on labeled data as well as neglect negative pairs on unlabeled data. Experimental results show that our learning strategies can improve the classification model by a large margin and outperform state-of-theart methods.

Deep learning has become a powerful tool for Mars exploration. Mars terrain segmentation is an important Martian vision task, which is the base of rover autonomous planning and safe driving. However, existing deep-learning-based terrain segmentation methods face two problems: one is the lack of sufficient detailed and high-confidence annotations, and the other is the over-reliance of models on annotated training data. In this paper, we address these two problems from the perspective of joint data and method design. We first present a new Mars terrain segmentation dataset which contains 6K high-resolution images and is sparsely annotated based on confidence, ensuring the high quality of labels. Then to learn from this sparse data, we propose a representation-learning-based framework for Mars terrain segmentation, including a self-supervised learning stage (for pre-training) and a semi-supervised learning stage (for fine-tuning). Specifically, for self-supervised learning, we design a multi-task mechanism based on the masked image modeling (MIM) concept to emphasize the texture information of images. For semi-supervised learning, since our dataset is sparsely annotated, we encourage the model to excavate the information of unlabeled area in each image by generating and utilizing pseudo-labels online. We name our dataset and method Self-Supervised and Semi-Supervised Segmentation for Mars (S 5 Mars). Experimental results show that our method can outperform state-of-the-art approaches and improve terrain segmentation performance by a large margin. Our project is available at https://jhang2020.github.io/S5Mars.github.io/.

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With the progress of Mars exploration, numerous Mars image data are collected and need to be analyzed. However, due to the severe train-test gap and quality distortion of Martian data, the performance of existing computer vision models is unsatisfactory. In this paper, we introduce a semi-supervised framework for machine vision on Mars and try to resolve two specific tasks: classification and segmentation. Contrastive learning is a powerful representation learning technique. However, there is too much information overlap between Martian data samples, leading to a contradiction between contrastive learning and Martian data. Our key idea is to reconcile this contradiction with the help of annotations and further take advantage of unlabeled data to improve performance. For classification, we propose to ignore inner-class pairs on labeled data as well as neglect negative pairs on unlabeled data, forming supervised inter-class contrastive learning and unsupervised similarity learning. For segmentation, we extend supervised inter-class contrastive learning into an element-wise mode and use online pseudo labels for supervision on unlabeled areas. Experimental results show that our learning strategies can improve the classification and segmentation models by a large margin and outperform state-of-the-art approaches.

Contrastive learning has been proven beneficial for self-supervised skeleton-based action recognition. Most contrastive learning methods utilize carefully designed augmentations to generate different movement patterns of skeletons for the same semantics. However, it is still a pending issue to apply strong augmentations, which distort the images/skeletons’ structures and cause semantic loss, due to their resulting unstable training. In this paper, we investigate the potential of adopting strong augmentations and propose a general hierarchical consistent contrastive learning framework (HiCLR) for skeleton-based action recognition. Specifically, we first design a gradual growing augmentation policy to generate multiple ordered positive pairs, which guide to achieve the consistency of the learned representation from different views. Then, an asymmetric loss is proposed to enforce the hierarchical consistency via a directional clustering operation in the feature space, pulling the representations from strongly augmented views closer to those from weakly augmented views for better generalizability. Meanwhile, we propose and evaluate three kinds of strong augmentations for 3D skeletons to demonstrate the effectiveness of our method. Extensive experiments show that HiCLR outperforms the state-of-the-art methods notably on three large-scale datasets, i.e., NTU60, NTU120, and PKUMMD. Our project is publicly available at: https://jhang2020.github.io/Projects/HiCLR/HiCLR.html.