MCML Munich AI Day 2026

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, underpinning a diverse set of real-world applications. The task of FGIA concerns visual objects from subordinate categories, e.g., species of birds or models of cars. Capitalizing on advances in Large Vision-Language Models (LVLMs), in recent years we have witnessed remarkable progress on a variety of FGIA problems. In this talk we review representative examples in the context of recognition and retrieval alongside other key issues such as publicly available benchmark datasets, domain-specific applications, and connections with other modalities including text and audio. We conclude by highlighting several research directions and open problems.

In recent years, machine learning (ML) and artificial intelligence (AI) methods have begun to play a more and more enabling role in the sciences and in industry. In particular, the advent of large and/or complex data corpora has given rise to new technological challenges and possibilities. In his talk, Müller will touch upon the topic of ML applications in the sciences, in particular in physics and chemistry. He will also discuss possibilities for extracting information from machine learning models to further our understanding by explaining nonlinear ML models. Finally, Müller will briefly discuss perspectives and limitations.

Geoinformation derived from Earth observation (EO) satellite data is indispensable for tackling grand societal challenges, such as urbanization, climate change, and the United Nations Sustainable Development Goals (SDGs). Furthermore, Earth observation has irreversibly arrived in the Big Data era, e.g. with ESA’s Sentinel satellites and with the blooming of NewSpace companies. This brings both tremendous opportunities and unprecedented analytical challenges. This talk showcases how innovative AI methods and big data analytics solutions can significantly improve the retrieval of large-scale geo-information from Earth observation data, and consequently lead to breakthroughs in geoscientific and environmental research. It also discusses emerging frontiers where AI, EO, and sustainability converge to support actionable insights for a more resilient planet.

Artificial Intelligence (AI) and Machine Learning (ML) methods are currently a “hot topic” in medicine. The driver of AI or ML method employment, in medicine as well as in other domains, is the availability of digital data. Here, the potential of delivering more objective, precise, and personalized medical diagnosis and care decisions is by far not reached. The reason is that the current healthcare data infrastructure, both nationally and internationally, lacks interoperability and interfaces on several different levels (individual, institutional, device, and provider level, just to name a few). The health data infrastructure for future healthcare needs to address this. One favored solution is “personal health dataspaces”, which put individuals at the center of health data (figure) and create new opportunities for AI and ML applications.
The talk will present the core idea of personal health dataspaces and hint at some opportunities for the future AI-driven “digital” healthcare system that emerge from it. It will open up new possibilities in healthcare, which will hopefully contribute to delivering more objective, precise, and personalized medical diagnosis and care decisions.