4 citations · 5 across the 6 of their papers we have counts for
6 papers
CAST: Clustering Self-Attention using Surrogate Tokens for Efficient Transformers
Adjorn van Engelenhoven, Nicola Strisciuglio, Estefanía Talavera
The Transformer architecture has shown to be a powerful tool for a wide range of tasks. It is based on the self-attention mechanism, which is an inherently computationally expensiv…
Regressing Transformers for Data-efficient Visual Place Recognition
María Leyva-Vallina, Nicola Strisciuglio, Nicolai Petkov
Visual place recognition is a critical task in computer vision, especially for localization and navigation systems. Existing methods often rely on contrastive learning: image descr…
What do neural networks learn in image classification? A frequency shortcut perspective
Shunxin Wang, Raymond Veldhuis, Christoph Brune +1
Frequency analysis is useful for understanding the mechanisms of representation learning in neural networks (NNs). Most research in this area focuses on the learning dynamics of NN…
DFM-X: Augmentation by Leveraging Prior Knowledge of Shortcut Learning
Shunxin Wang, Christoph Brune, Raymond Veldhuis +1
Neural networks are prone to learn easy solutions from superficial statistics in the data, namely shortcut learning, which impairs generalization and robustness of models. We propo…
Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space
Ioana Mazilu, Shunxin Wang, Sven Dummer +3
Though modern microscopes have an autofocusing system to ensure optimal focus, out-of-focus images can still occur when cells within the medium are not all in the same focal plane,…
Data-efficient Large Scale Place Recognition with Graded Similarity Supervision
Maria Leyva-Vallina, Nicola Strisciuglio, Nicolai Petkov
Visual place recognition (VPR) is a fundamental task of computer vision for visual localization. Existing methods are trained using image pairs that either depict the same place or…