Learning Robot Vision under Insufficient Data

Machine learning is used today in a wide variety of applications, especially within computer vision, robotics, and autonomous systems. Example use cases include detecting people or other objects using cameras in autonomous vehicles, or navigating robots through collision-free paths to solve different tasks. The flexibility of machine learning is attractive as it can be applied to a wide variety of challenging tasks, without detailed prior knowledge of the problem domain. However, training machine learning models requires vast amounts of data, which leads to a significant manual effort, both for collecting the data and for annotating it. In this thesis, we study and develop methods for training machine learning models under in-sufficient data within computer vision, robotics, and autonomous systems, for the purpose of reducing the manual effort. In summary, we study (1) weakly-supervised learning for reducing the annotation cost, (2) methods for reducing model bias under highly imbalanced training data,(3) methods for obtaining trustworthy uncertainty estimates, and (4) the use of simulated and semi-virtual environments for reducing the amount of real-world data in reinforcement learning. In the first part of this thesis, we investigate how weakly-supervised learning can be used within image segmentation. In contrast to fully supervised learning, weakly-supervised learning uses a weaker form of annotation, which reduces the annotation effort. Typically, in image segmentation, each object needs to be precisely annotated in every image on the pixel level. Creating this type of annotation is both time consuming and costly. In weakly-supervised segmentation, however, the only information required is which objects are depicted in the images. This significantly reduces the annotation time. In Papers A and B, we propose two loss functions for improving the predicted object segmentations, especially their contours, in weakly-supervised segmentation. In the next part of the thesis, we tackle class imbalance in image classification. During data collection, some classes naturally occur more frequently than others, which leads to an imbalance in the amount of data between the different classes. Models trained on such datasets may become biased towards the more common classes. Overcoming this effect by collecting more data of the rare classes may take a very long time. Instead, we develop an ensemble method for image classification in Paper C, which is unbiased despi