3 citations · 3 across the 7 of their papers we have counts for
4 papers · 1 filter
Quantifying Ambiguity in Categorical Annotations: A Measure and Statistical Inference Framework
Christopher Klugmann, Daniel Kondermann
Human-generated categorical annotations frequently produce empirical response distributions (soft labels) that reflect ambiguity rather than simple annotator error. We introduce an…
Decomposing Visual Classification: Assessing Tree-Based Reasoning in VLMs
Sary Elmansoury, Islam Mesabah, Gerrit Großmann +4
Vision language models (VLMs) excel at zero-shot visual classification, but their performance on fine-grained tasks and large hierarchical label spaces is understudied. This paper…
From Label Error Detection to Correction: A Modular Framework and Benchmark for Object Detection Datasets
Sarina Penquitt, Jonathan Klees, Rinor Cakaj +3
Object detection has advanced rapidly in recent years, driven by increasingly large and diverse datasets. However, label errors often compromise the quality of these datasets and a…
Minority Reports: Balancing Cost and Quality in Ground Truth Data Annotation
Hsuan Wei Liao, Christopher Klugmann, Daniel Kondermann +1
High-quality data annotation is an essential but laborious and costly aspect of developing machine learning-based software. We explore the inherent tradeoff between annotation accu…