6 citations · 6 across the 9 of their papers we have counts for
4 papers · 1 filter
Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Context
Shashank Agnihotri, Julia Grabinski, Margret Keuper
Pixel-wise predictions are required in a wide variety of tasks such as image restoration, image segmentation, or disparity estimation. Common models involve several stages of data…
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix
Shashank Agnihotri, Kanchana Vaishnavi Gandikota, Julia Grabinski +2
Following their success in visual recognition tasks, Vision Transformers(ViTs) are being increasingly employed for image restoration. As a few recent works claim that ViTs for imag…
As large as it gets: Learning infinitely large Filters via Neural Implicit Functions in the Fourier Domain
Julia Grabinski, Janis Keuper, Margret Keuper
Recent work in neural networks for image classification has seen a strong tendency towards increasing the spatial context. Whether achieved through large convolution kernels or sel…
Fix your downsampling ASAP! Be natively more robust via Aliasing and Spectral Artifact free Pooling
Julia Grabinski, Steffen Jung, Janis Keuper +1
Convolutional Neural Networks (CNNs) are successful in various computer vision tasks. From an image and signal processing point of view, this success is counter-intuitive, as the i…