15 citations · 47 across the 10 of their papers we have counts for
10 papers
Object Detection Benchmarks are Incomplete: The Role of Label Errors and Annotation Uncertainty
Sarina Penquitt, Jonathan Klees, Antonia van Betteray +4
While object detection has advanced through improved architectures and open-vocabulary models, we provide strong evidence that benchmark quality is limited by annotation incomplete…
Annotating Ambiguous Images: General Annotation Strategy for High-Quality Data with Real-World Biomedical Validation
Lars Schmarje, Vasco Grossmann, Claudius Zelenka +2
In the field of image classification, existing methods often struggle with biased or ambiguous data, a prevalent issue in real-world scenarios. Current strategies, including semi-s…
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…
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…
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…
Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation
Lars Schmarje, Vasco Grossmann, Claudius Zelenka +8
High-quality data is necessary for modern machine learning. However, the acquisition of such data is difficult due to noisy and ambiguous annotations of humans. The aggregation of…
Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy
Lars Schmarje, Johannes Brünger, Monty Santarossa +3
Deep learning has been successfully applied to many classification problems including underwater challenges. However, a long-standing issue with deep learning is the need for large…
Life is not black and white -- Combining Semi-Supervised Learning with fuzzy labels
Lars Schmarje, Reinhard Koch
The required amount of labeled data is one of the biggest issues in deep learning. Semi-Supervised Learning can potentially solve this issue by using additional unlabeled data. How…
A data-centric approach for improving ambiguous labels with combined semi-supervised classification and clustering
Lars Schmarje, Monty Santarossa, Simon-Martin Schröder +5
Consistently high data quality is essential for the development of novel loss functions and architectures in the field of deep learning. The existence of such data and labels is us…
Beyond Cats and Dogs: Semi-supervised Classification of fuzzy labels with overclustering
Lars Schmarje, Johannes Brünger, Monty Santarossa +3
A long-standing issue with deep learning is the need for large and consistently labeled datasets. Although the current research in semi-supervised learning can decrease the require…