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Curriculum learning techniques are a viable solution for improving theaccuracy of automatic models, by replacing the traditional random training withan easy-to-hard strategy. However, the standard curriculum methodology does notautomatically provide improved results, but it is constrained by multipleelements like the data distribution or the proposed model. In this paper, weintroduce a novel curriculum sampling strategy which takes into considerationthe 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 thehuman time required for solving a visual search task. We consider this kind ofdifficulty metric to be better suited for solving general problems, as it isnot based on certain task-dependent elements, but more on the context of eachimage. We ensure the diversity during training, giving higher priority toelements from less visited classes. We conduct object detection and instancesegmentation experiments on Pascal VOC 2007 and Cityscapes data sets,surpassing both the randomly-trained baseline and the standard curriculumapproach. We prove that our strategy is very efficient for unbalanced datasets, leading to faster convergence and more accurate results, when othercurriculum-based strategies fail.

Curriculum Learning with Diversity for Supervised Computer Vision Tasks