It is natural to represent objects in terms of their parts. This has the potential to improve the
performance of algorithms for object recognition and segmentation but can also help for …

Segmenting object parts such as cup handles and animal bodies is important in many real-
world applications but requires more annotation effort. The largest dataset nowadays …

Adversarial attacks can easily fool object recognition systems based on deep neural
networks (DNNs). Although many defense methods have been proposed in recent years …

Is analogical reasoning a task that must be learned to solve from scratch by applying deep
learning models to massive numbers of reasoning problems? Or are analogies solved by …

In this paper, we are interested in addressing the problem of damage assessment for
vehicles, such as cars. This task requires not only detecting the location and the extent of the …

Despite the great potential of convolutional neural networks (CNNs) in various tasks, the
resource-hungry nature greatly hinders their wide deployment in cost-sensitive and low …

Hierarchical segmentation entails creating segmentations at varying levels of granularity.
We introduce the first hierarchical semantic segmentation dataset with subpart annotations …