3 papers
cs.LG2026
DirPA: Addressing Prior Shift in Imbalanced Few-shot Crop-type Classification
Joana Reuss, Ekaterina Gikalo, Marco Körner
Real-world agricultural monitoring is often hampered by severe class imbalance and high label acquisition costs, resulting in significant data scarcity. In few-shot learning (FSL)…
cs.LG2025
Benchmarking for Practice: Few-Shot Time-Series Crop-Type Classification on the EuroCropsML Dataset
Joana Reuss, Jan Macdonald, Simon Becker +4
Accurate crop-type classification from satellite time series is essential for agricultural monitoring. While various machine learning algorithms have been developed to enhance perf…
cs.CL2025
TUM-MiKaNi at SemEval-2025 Task 3: Towards Multilingual and Knowledge-Aware Non-factual Hallucination Identification
Miriam Anschütz, Ekaterina Gikalo, Niklas Herbster +1
Hallucinations are one of the major problems of LLMs, hindering their trustworthiness and deployment to wider use cases. However, most of the research on hallucinations focuses on…