2.1k citations · 3.1k across the 8 of their papers we have counts for
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ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis +3
Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more…
One-Shot Instance Segmentation
Claudio Michaelis, Ivan Ustyuzhaninov, Matthias Bethge +1
We tackle the problem of one-shot instance segmentation: Given an example image of a novel, previously unknown object category, find and segment all objects of this category within…
One-shot Texture Segmentation
Ivan Ustyuzhaninov, Claudio Michaelis, Wieland Brendel +1
We introduce one-shot texture segmentation: the task of segmenting an input image containing multiple textures given a patch of a reference texture. This task is designed to turn t…
One-Shot Segmentation in Clutter
Claudio Michaelis, Matthias Bethge, Alexander S. Ecker
We tackle the problem of one-shot segmentation: finding and segmenting a previously unseen object in a cluttered scene based on a single instruction example. We propose a novel dat…