Towards Rhino-AR: A System for Real-Time 3D Human Pose Estimation and Volumetric Scene Integration on Embedded AR Headsets
Holland, L. V., Kaspers, N., Dengler, N., Stotko, P., Bennewitz, M., & Klein, R. (2025). Towards Rhino-AR: A System for Real-Time 3D Human Pose Estimation and Volumetric Scene Integration on Embedded AR Headsets. In 2025 11th International Conference on Virtual Reality (ICVR) (pp. 135–143). 2025 11th International Conference on Virtual Reality (ICVR). IEEE. https://doi.org/10.1109/icvr66534.2025.11172603
SAFT: Shape and Appearance of Fabrics from Template via Differentiable Physical Simulations from Monocular Video
Stotko, D., & Klein, R. (2025). SAFT: Shape and Appearance of Fabrics from Template via Differentiable Physical Simulations from Monocular Video (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2509.08828
Canonical Rank Adaptation: An Efficient Fine-Tuning Strategy for Vision Transformers
Veeramacheneni, L., Wolter, M., Kuehne, H., & Gall, J. (2025). Canonical Rank Adaptation: An Efficient Fine-Tuning Strategy for Vision Transformers. Forty-Second International Conference on Machine Learning. URL: https://openreview.net/forum?id=vexHifrbJg
Improving the Quality of Unstructured Cancer Data Using Large Language Models: A German Oncological Case Study
Mou, Y., Lehmkuhl, J., Sauerbrunn, N., Köchel, A., Panse, J., Truh, D., Sowe, S., Brümmendorf, T., & Decker, S. (2024). Improving the quality of unstructured cancer data using large language models: A German oncological case study. Studies in Health Technology and Informatics, 316, 685–689. https://doi.org/10.3233/SHTI240507
GroupMamba: Efficient Group-Based Visual State Space Model
Shaker, A., Wasim, S. T., Khan, S., Gall, J., & Khan, F. S. (2025). GroupMamba: Efficient group-based visual state space model [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2407.13772
Video-Panda: Parameter-efficient Alignment for Encoder-free Video-Language Models
Yi, J., Wasim, S. T., Luo, Y., Naseer, M., & Gall, J. (2025). Video‑Panda: Parameter‑efficient alignment for encoder‑free video‑language models [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2412.18609
STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection
elayudhan, D., Ahmed, A. H., Alansari, M., Gour, N., Behouch, A., Hassan, T., Wasim, S. T., Maalej, N., Naseer, M., Gall, J., Bennamoun, M., Damiani, E., & Werghi, N. (2025). STING‑BEE: Towards vision‑language model for real‑world X‑ray baggage security inspection [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2504.02823
Metamizer: A versatile neural optimizer for fast and accurate physics simulations
Wandel, N., Schulz, S., & Klein, R. (2025). Metamizer: A versatile neural optimizer for fast and accurate physics simulations [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2410.19746
Dataset pruning for targeted knowledge distillation
Werning, A., & Haeb-Umbach, R. (2023). UPB-NT submission to DCASE24: Dataset pruning for targeted knowledge distillation [Technical report]. Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge 2024. https://dcase.community/documents/challenge2024/technical_reports/DCASE2024_Werning_48_t1.pdf
Message-Passing on Directed Acyclic Graphs Prevents Over-Smoothing
Roth, A., Bause, F., Kriege, N. M., & Liebig, T. (2024). Message-passing on directed acyclic graphs prevents over-smoothing. In Proceedings of the 21st International Workshop on Mining and Learning with Graphs (MLG@ECML-PKDD 2024). https://mlg-europe.github.io/2024/papers/35/Submission/DA_MPNNs_MLG2024.pdf
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