MCML - Research Blog

10.04.2025

Text2Loc: A Smarter Way to Navigate With Words

MCML Research Insight - With Yan Xia, Zifeng Ding and Daniel Cremers

Imagine standing in an unfamiliar part of a city, no GPS in sight. All you can say is, “I’m west of a green building, near a black garage.” That might be vague to a machine, but Text2Loc understands you perfectly. With this powerful new system, AI can find your exact location in a 3D map - just from …

CUPS: Teaching AI to Understand Scenes Without Human Labels

MCML Research Insight - With Christoph Reich, Nikita Araslanov, and Daniel Cremers

What matters now

Understanding the location and semantics of objects in a scene is a significant task, enabling robots to navigate through complex environments or facilitating autonomous driving. Recent AI models for understanding scenes from images require significant guidance from humans in the form of pixel-level annotations to achieve accurate …

Beyond the Black Box: Choosing the Right Feature Importance Method

MCML Research Insight - With Fiona Katharina Ewald, Ludwig Bothmann, Giuseppe Casalicchio and Bernd Bischl

Machine learning models make powerful predictions, but can we really trust them if we don’t understand how they work? Global feature importance methods help us discover which factors really matter - but choosing the wrong method can lead to misleading conclusions. To see why this is important, consider a real-world example from medicine.