3 accepted papers
Despite excellent average-case performance of many image classifiers, their performance can substantially deteriorate on semantically coherent subgroups of the data that were under-represented in the training data. These systematic errors can impact both fairness for demographic minority groups as w…
Convolutional neural networks (CNNs) learn to extract representations of complex features, such as object shapes and textures to solve image recognition tasks. Recent work indicates that CNNs trained on ImageNet are biased towards features that encode textures and that these alone are sufficient to…
We present an approach for recognizing indoor scenes in object constellations that require object search by a mobile robot, as they cannot be captured from a single viewpoint. In our approach that we call Active Scene Recognition (ASR), robots predict object poses from learnt spatial relations that…