
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
OoDIS: Anomaly Instance Segmentation Benchmark
Nekrasov, A., Zhou, R., Ackermann, M., Hermans, A., Leibe, B., & Rottmann, M. (2024). OoDIS: Anomaly instance segmentation and detection benchmark (Version 2) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2406.11835
LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction
Er Jin, Qihui Feng, Yongli Mou, Gerhard Lakemeyer, Stefan Decker, Oliver Simons and Johannes Stegmaier, LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction. Proceedings of the AAAI Conference on Artificial Intelligence. 39, 4 (Apr. 2025), 4129-4137. DOI:https://doi.org/10.1609/aaai.v39i4.32433.
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