  Curriculum learning techniques are a viable solution for improving the
accuracy of automatic models, by replacing the traditional random training with
an easy-to-hard strategy. However, the standard curriculum methodology does not
automatically provide improved results, but it is constrained by multiple
elements like the data distribution or the proposed model. In this paper, we
introduce a novel curriculum sampling strategy which takes into consideration
the diversity of the training data together with the difficulty of the inputs.
We determine the difficulty using a state-of-the-art estimator based on the
human time required for solving a visual search task. We consider this kind of
difficulty metric to be better suited for solving general problems, as it is
not based on certain task-dependent elements, but more on the context of each
image. We ensure the diversity during training, giving higher priority to
elements from less visited classes. We conduct object detection and instance
segmentation experiments on Pascal VOC 2007 and Cityscapes data sets,
surpassing both the randomly-trained baseline and the standard curriculum
approach. We prove that our strategy is very efficient for unbalanced data
sets, leading to faster convergence and more accurate results, when other
curriculum-based strategies fail.
