Tom Oswald Burgert
Tom Oswald Burgert
Doctoral Researcher
Tom Burgert
Deep Learning for Earth Observation: On Feature Reliance, Representation Learning and Label Noise
Leonard Hackel, Tom Burgert, Begüm Demir
How Much of a Model Do We Need? Redundancy and Slimmability in Remote Sensing Foundation Models
Tom Burgert, Julia Henkel, Begüm Demir
Noise-Adaptive Regularization for Robust Multi-Label Remote Sensing Image Classification
Tom Burgert, Leonard Hackel, Paolo Rota, Begüm Demir
Rank-based Geographical Regularization: Revisiting Contrastive Self-Supervised Learning for Multispectral Remote Sensing Imagery
Tom Burgert, Oliver Stoll, Paolo Rota, Begüm Demir
ImageNet-trained CNNs are not biased towards texture: Revisiting feature reliance through controlled suppression
Rethinking how models "see"
Congratulations to BIFOLD researchers Tom Burgert, Oliver Stoll, and Begüm Demir from TU Berlin, and Paolo Rota from the University of Trento. They published a new study, that revisits a central claim in computer vision: so-called convolutional neural networks (CNNs) primarily rely on texture, rather than object shape, to recognize images. The publication was accepted as an oral presentation at NeurIPS 2025.