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Public Lecture by Prof Michael Felsberg: Deep Learning for Visual Perception
February 13 @ 5:30 pm UTC+2
Deep learning with vast amounts of data has revolutionized the field of computer vision. Visual perceptions for robotics, however, requires to go beyond the developed methods due to its specific requirements: The amount of data is limited and needs to be processed on-the-fly, annotation is incomplete or missing, measurements are sparse and of varying level of certainty, and detection and segmentation tasks need to be performed in real-time. Modern approaches to these visual perception problems will be presented and explained: online learning, weakly supervised and reinforcement learning, certainty-adaptive data densification, anomaly detection, and modulation-based discriminative segmentation. The results of these methods achieve state-of-the-art results on major benchmarks, such as VOT, DAVIS, Kitti, and CARLA, but require enormous computational resources during the method development.