Johannes Meier — accepted AI-conference papers

2025

MonoCT: Overcoming Monocular 3D Detection Domain Shift with Consistent Teacher Models

Johannes Meier, Louis Inchingolo, Oussema Dhaouadi, Yan Xia, Jacques Kaiser, Daniel Cremers

ICRA 2025

We tackle the problem of monocular 3D object detection across different sensors, environments, and camera setups. In this paper, we introduce a novel unsupervised domain adaptation approach, MonoCT, that generates highly accurate pseudo labels for self-supervision. Inspired by our observation that a

Cited by 9 Source ↗Scholar ↗

2023

NIFF: Alleviating Forgetting in Generalized Few-Shot Object Detection via Neural Instance Feature Forging

Karim Guirguis, Johannes Meier, George Eskandar, Matthias Kayser, Bin Yang, Jürgen Beyerer

CVPR 2023poster

Privacy and memory are two recurring themes in a broad conversation about the societal impact of AI. These concerns arise from the need for huge amounts of data to train deep neural networks. A promise of Generalized Few-shot Object Detection (G-FSOD), a learning paradigm in AI, is to alleviate the…

Cited by 16 Source ↗PDF ↗Scholar ↗