Mert Keser — 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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2021

Content Disentanglement for Semantically Consistent Synthetic-to-Real Domain Adaptation

Mert Keser, Artem Savkin, Federico Tombari

IROS 2021poster

Synthetic data generation is an appealing approach to generate novel traffic scenarios in autonomous driving. However, deep learning perception algorithms trained solely on synthetic data encounter serious performance drops when they are tested on real data. Such performance drops are commonly attri…

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