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20172020
most citedNeural system identification for large populations separating "what" and "where"

48 citations · 76 across the 3 of their papers we have counts for

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6 papers · 1 filter

cs.CV202015 cited

Assessing out-of-domain generalization for robust building damage detection

Vitus Benson, Alexander Ecker

An important step for limiting the negative impact of natural disasters is rapid damage assessment after a disaster occurred. For instance, building damage detection can be automat…

cs.CV2019

Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos +5

The ability to detect objects regardless of image distortions or weather conditions is crucial for real-world applications of deep learning like autonomous driving. We here provide…

cs.CV2018

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…

cs.CV2018

Diverse feature visualizations reveal invariances in early layers of deep neural networks

Santiago A. Cadena, Marissa A. Weis, Leon A. Gatys +2

Visualizing features in deep neural networks (DNNs) can help understanding their computations. Many previous studies aimed to visualize the selectivity of individual units by findi…

cs.CV2018

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…

cs.CV201713 cited

Synthesising Dynamic Textures using Convolutional Neural Networks

Christina M. Funke, Leon A. Gatys, Alexander S. Ecker +1

Here we present a parametric model for dynamic textures. The model is based on spatiotemporal summary statistics computed from the feature representations of a Convolutional Neural…