Learning from Disagreement in Object Detection (SoSe 2026)
Project Description:
In machine learning, the concept of "ground truth" is often used synonymously with human annotations in computer vision. However, how can a definitive ground truth exist if multiple annotators disagree?
The standard approach is to aggregate a single ground truth from these conflicting labels. In this project, we take a different perspective: we view these disagreements as a valuable supervision signal that captures inherent variation. Throughout the project, we will explore neural network designs capable of learning directly from disagreement, adapt evaluation and diagnostic tools for this task, and test our methods on a selection of datasets from various domains.
Learning Objectives:
- Work with modern deep learning frameworks.
- Develop modular and maintainable machine learning code.
- Design and execute rigorous experiments.
- Investigate and implement novel model architectures.
Prerequisites:
- Successful completion of the course "Deep Learning for Computer Vision".