  This work investigates the use of class-level difficulty factors in
multi-label classification problems for the first time. Four class-level
difficulty factors are proposed: frequency, visual variation, semantic
abstraction, and class co-occurrence. Once computed for a given multi-label
classification dataset, these difficulty factors are shown to have several
potential applications including the prediction of class-level performance
across datasets and the improvement of predictive performance through
difficulty weighted optimisation. Significant improvements to mAP and AUC
performance are observed for two challenging multi-label datasets (WWW Crowd
and Visual Genome) with the inclusion of difficulty weighted optimisation. The
proposed technique does not require any additional computational complexity
during training or inference and can be extended over time with inclusion of
other class-level difficulty factors.
