Self-supervised learning (SSL) has gained widespread attention in the remote sensing (RS) and earth observation (EO) communities owing to its ability learn task-agnostic representations without human-annotated labels. Nevertheless, most existing RS SSL methods are limited either global semantic separable or local spatial perceptible representations. We argue that this strategy is suboptimal rea...

For the safe and successful navigation of autonomous vehicles in unstructured environments, traversability terrain should vary based on driving capabilities vehicles. Actual experience can be utilized a self-supervised fashion to learn vehicle-specific traversability. However, existing methods for learning are not highly scalable various In this work, we introduce framework traversability, whic...

Traditional supervised classifiers use only labeled data (features/label pairs) as the training set, while the unlabeled data is used as the testing set. In practice, it is often the case that the labeled data is hard to obtain and the unlabeled data contains the instances that belong to the predefined class beyond the labeled data categories. This problem has been widely studied in recent year...

Semi-supervised learning methods address the problem of building classifiers when labeled data is scarce. Text classification is often augmented by rich set of labeled features representing a particular class. As tuple level labling is resource consuming, semi-supervised and weakly supervised learning methods are explored recently. Compared to labeling data instances (documents), feature labeli...