  Attempts of learning from hierarchical taxonomies in computer vision have
been mostly focusing on image classification. Though ways of best harvesting
learning improvements from hierarchies in classification are far from being
solved, there is a need to target these problems in other vision tasks such as
object detection. As progress on the classification side is often dependent on
hierarchical cross-entropy losses, novel detection architectures using sigmoid
as an output function instead of softmax cannot easily apply these advances,
requiring novel methods in detection. In this work we establish a theoretical
framework based on probability and set theory for extracting parent predictions
and a hierarchical loss that can be used across tasks, showing results across
classification and detection benchmarks and opening up the possibility of
hierarchical learning for sigmoid-based detection architectures.
