Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks. - Jörg Wagner; Jan Mathias Köhler; Tobias Gindele; Leon Hetzel; Jakob Thaddäus Wiedemer; Sven Behnke (CVPR, 2019)

Few-Shot Supervised Prototype Alignment for Pedestrian Detection on Fisheye Images. - Thaddäus Wiedemer; Stefan Wolf; Arne Schumann; Kaisheng Ma; Jürgen Beyerer (CVPR Workshops, 2022)

Compositional Generalization from First Principles. - Thaddäus Wiedemer; Prasanna Mayilvahanan; Matthias Bethge; Wieland Brendel (NeurIPS, 2023)

Does CLIP's generalization performance mainly stem from high train-test similarity? - Prasanna Mayilvahanan; Thaddäus Wiedemer; Evgenia Rusak; Matthias Bethge; Wieland Brendel (ICLR, 2024)

Provable Compositional Generalization for Object-Centric Learning. - Thaddäus Wiedemer; Jack Brady; Alexander Panfilov; Attila Juhos; Matthias Bethge; Wieland Brendel (ICLR, 2024)

Scale Learning in Scale-Equivariant Convolutional Networks. - Mark Basting; Robert-Jan Bruintjes; Thaddäus Wiedemer; Matthias Kümmerer; Matthias Bethge; Jan van Gemert (VISIGRAPP (2): VISAPP, 2024)

In Search of Forgotten Domain Generalization. - Prasanna Mayilvahanan; Roland S. Zimmermann; Thaddäus Wiedemer; Evgenia Rusak; Attila Juhos; Matthias Bethge; Wieland Brendel (ICLR, 2025)

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws. - Prasanna Mayilvahanan; Thaddäus Wiedemer; Sayak Mallick; Matthias Bethge; Wieland Brendel (CoRR, 2025)

Pretraining Frequency Predicts Compositional Generalization of CLIP on Real-World Tasks. - Thaddäus Wiedemer; Yash Sharma 001; Ameya Prabhu; Matthias Bethge; Wieland Brendel (CoRR, 2025)

VGGSounder: Audio-Visual Evaluations for Foundation Models. - Daniil Zverev; Thaddäus Wiedemer; Ameya Prabhu; Matthias Bethge; Wieland Brendel; A. Sophia Koepke (CoRR, 2025)