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.

中文翻译：

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用于监督计算机视觉任务的多样性课程学习

课程学习技术是提高自动模型准确性的可行解决方案，通过用易到难的策略代替传统的随机训练。但是，标准课程方法不会自动提供改进的结果，而是受到数据分布或建议模型等多个元素的约束。在本文中，我们引入了一种新的课程抽样策略，该策略考虑了训练数据的多样性以及输入的难度。我们根据解决视觉搜索任务所需的人工时间，使用最先进的估算器来确定难度。我们认为这种难度度量更适合解决一般问题，因为它不是基于某些依赖于任务的元素，而是更多地基于每个图像的上下文。我们确保训练期间的多样性，对访问较少的类中的元素给予更高的优先级。我们在 Pascal VOC 2007 和 Cityscapes 数据集上进行了对象检测和实例分割实验，超越了随机训练的基线和标准课程方法。我们证明了我们的策略对于不平衡的数据集非常有效，当其他基于课程的策略失败时，会导致更快的收敛和更准确的结果。