Interpretable and fine-grained visual explanations for convolutional neural networks J Wagner, JM Kohler, T Gindele, L Hetzel, JT Wiedemer, S Behnke Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 171 | 2019 |
Compositional generalization from first principles T Wiedemer, P Mayilvahanan, M Bethge, W Brendel Advances in Neural Information Processing Systems 36, 2024 | 21* | 2024 |
Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity? P Mayilvahanan, T Wiedemer, E Rusak, M Bethge, W Brendel arXiv preprint arXiv:2310.09562, 2023 | 10 | 2023 |
Few-shot supervised prototype alignment for pedestrian detection on fisheye images T Wiedemer, S Wolf, A Schumann, K Ma, J Beyerer Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 8 | 2022 |
Provable Compositional Generalization for Object-Centric Learning T Wiedemer, J Brady, A Panfilov, A Juhos, M Bethge, W Brendel arXiv preprint arXiv:2310.05327, 2023 | 5 | 2023 |
Method and device for ascertaining an explanation map J Wagner, T Gindele, JM Koehler, JT Wiedemer, L Hetzel US Patent 11,645,828, 2023 | | 2023 |
Scale Learning in Scale-Equivariant Convolutional Networks M Basting, RJ Bruintjes, T Wiedemer, M Kümmerer, M Bethge, ... Proceedings Copyright 567, 574, 2023 | | 2023 |
In Search of Forgotten Domain Generalization P Mayilvahanan, RS Zimmermann, T Wiedemer, E Rusak, A Juhos, ... ICML 2024 Workshop on Foundation Models in the Wild, 0 | | |