Efficient Active Online Learning for 3D Reconstruction and Scene Understanding

• Applicants: Professor Dr. Daniel Cremers; Professor Dr. Rudolph Triebel
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: from 2014 to 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 260350367

Machine learning algorithms have become an important building block in modern computer vision systems. However, most of these systems are based on offline learning, i.e. learning is done only once before system deployment, and future adaptations to unobserved circumstances are not considered. Furthermore, the amount of labeled training data required for the learning task is usually very high, because the system has no possibility to select subsets of the training data that are particularly suited for the required classification task. Quite in contrast, human perception strongly relies on our capacity to constantly learn and adapt our acquired knowledge to new environments and circumstances. The goal of this project is to address this challenge by developing novel learning methods that perform the learning task with a higher degree of autonomy than current state-of-the-art systems. Here, autonomy refers to the ability of the system to decide which kind of information is more useful for more efficient learning. To achieve this, we will develop an Active Learning system, in which learning is done in cycles: After an initial training phase the system is presented with new observations for classification. Among them it selects those that are most infomative. For these, labels are queried from a human and the so obtained labeled data are used in the next round of training. This will lead to two major improvements over current systems: first, the required amount of labeled training data will be significantly lower, because the used training data will be much more context-oriented. And second, the system will have a much higher capability to adapt to new situations or environments, because learning is done in an ongoing process. Our Active Learning system will be applied to two important research challenges in computer vision, namely 3D reconstruction and scene understanding, and the aim is to achieve substantial performance improvements in these areas using our proposed autonomous, i.e. in this case active learningapproach.

• DFG Programme: Priority Programmes
• Subproject of: SPP 1527: Autonomous Learning