6 citations · 6 across the 9 of their papers we have counts for
10 papers
Doppio: A Dataset for Contactless Weight Estimation of Falling Particles
Simon Kiefhaber, Jan-Martin O. Steitz, Julia Grabinski +5
Measuring the mass of powder, including falling particles, is a common task in industrial applications. While scales are effective for static measurements, many applications requir…
ReconSplat: Generalizable 3D Scene Reconstruction Beyond Observed Views
Giuseppe Stracquadanio, Kevin Raj, Julia Grabinski +1
We introduce ReconSplat, a feed-forward model for 3D scene reconstruction that aims to address the longstanding trade-off between plausible view generation for unobserved regions a…
Towards Class-wise Robustness Analysis
Tejaswini Medi, Julia Grabinski, Margret Keuper
While being very successful in solving many downstream tasks, the application of deep neural networks is limited in real-life scenarios because of their susceptibility to domain sh…
Beware of Aliases -- Signal Preservation is Crucial for Robust Image Restoration
Shashank Agnihotri, Julia Grabinski, Janis Keuper +1
Image restoration networks are usually comprised of an encoder and a decoder, responsible for aggregating image content from noisy, distorted data and to restore clean, undistorted…
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…
Robust Models are less Over-Confident
Julia Grabinski, Paul Gavrikov, Janis Keuper +1
Despite the success of convolutional neural networks (CNNs) in many academic benchmarks for computer vision tasks, their application in the real-world is still facing fundamental c…
FrequencyLowCut Pooling -- Plug & Play against Catastrophic Overfitting
Julia Grabinski, Steffen Jung, Janis Keuper +1
Over the last years, Convolutional Neural Networks (CNNs) have been the dominating neural architecture in a wide range of computer vision tasks. From an image and signal processing…