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

United We Learn Better: Harvesting Learning Improvements From Class Hierarchies Across Tasks