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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 traini...
Robust Visual Learning across Class Imbalance and Distributional Shift
Computer vision aims to equip machines with perceptual understanding—detecting, recognizing, localizing, and relating visual entities to existing sources of knowledge. Machine learning provides the mechanism: models learn representations and decision rules from data and are expected to generalize beyond the training distribution. These systems already support biodiversity monitoring, autonomous driving, and geospatial mapping. In practice, however, textbook assumptions break down: the concept space is vast, data is sparse and imbalanced, many categories are rare, and high-quality annotations are costly. In addition, deployment conditions shift over time—class frequencies and visual domai...
