activity
20222024
most citedIs one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation

11 citations · 19 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2024

UNCOVER: Unknown Class Object Detection for Autonomous Vehicles in Real-time

Lars Schmarje, Kaspar Sakman, Reinhard Koch +1

Autonomous driving (AD) operates in open-world scenarios, where encountering unknown objects is inevitable. However, standard object detectors trained on a limited number of base c…

cs.CV2024

Mind the Exit Pupil Gap: Revisiting the Intrinsics of a Standard Plenoptic Camera

Tim Michels, Daniel Mäckelmann, Reinhard Koch

Among the common applications of plenoptic cameras are depth reconstruction and post-shot refocusing. These require a calibration relating the camera-side light field to that of th…

cs.CV2023

Label Smarter, Not Harder: CleverLabel for Faster Annotation of Ambiguous Image Classification with Higher Quality

Lars Schmarje, Vasco Grossmann, Tim Michels +4

High-quality data is crucial for the success of machine learning, but labeling large datasets is often a time-consuming and costly process. While semi-supervised learning can help…

cond-mat.mtrl-sci20222 cited

Automated Classification of Nanoparticles with Various Ultrastructures and Sizes

Claudius Zelenka, Marius Kamp, Kolja Strohm +4

Accurately measuring the size, morphology, and structure of nanoparticles is very important, because they are strongly dependent on their properties for many applications. In this…

cs.CV20223 cited

Opportunistic hip fracture risk prediction in Men from X-ray: Findings from the Osteoporosis in Men (MrOS) Study

Lars Schmarje, Stefan Reinhold, Timo Damm +3

Osteoporosis is a common disease that increases fracture risk. Hip fractures, especially in elderly people, lead to increased morbidity, decreased quality of life and increased mor…

cs.CV20223 cited

Beyond Hard Labels: Investigating data label distributions

Vasco Grossmann, Lars Schmarje, Reinhard Koch

High-quality data is a key aspect of modern machine learning. However, labels generated by humans suffer from issues like label noise and class ambiguities. We raise the question o…