Edgar Heinert — accepted AI-conference papers

2026

Explaining, Verifying and Aligning Semantic Hierarchies in Vision-Language Model Embeddings

Gesina Schwalbe, Mert Keser, Moritz Bayerkuhnlein, Edgar Heinert, Annika Mütze, Marvin Keller +5

IJCAI 2026

Vision-language model (VLM) encoders such as CLIP enable strong retrieval and zero-shot classification in a shared image–text embedding space, yet the semantic organization of this space is rarely inspected. We present a post-hoc framework to explain, verify, and align the semantic hierarchies induc

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2026

PRIMU: Uncertainty Estimation for Novel Views in Gaussian Splatting from Primitive-Based Representations of Error and Coverage

Thomas Gottwald, Edgar Heinert, Peter Stehr, Chamuditha Jayanga Galappaththige, Matthias Rottmann

CVPR 2026

We introduce Primitive-based Representations of Uncertainty (PRIMU), a post-hoc uncertainty estimation (UE) framework for Gaussian Splatting (GS).Reliable UE is essential for deploying GS in safety-critical domains such as robotics and medicine.Existing approaches typically estimate Gaussian-primiti

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